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Financial analysis of Tesla research article

 Financial analysis of Tesla


Strategic Management analysis

Research on Financial Analysis of Tesla
Research on Financial Analysis of Tesla


Student No:0123456


MSc in Accounting and Finance with Data Analytics


Name: XYZ

 

Research on Financial Analysis of Tesla
Research on Financial Analysis of Tesla

Table of Contents

Overview: 3

Tesla: 3

Ansoff matrix: 3

Tesla Motors and Ansoff model: 4

Matrix comparison: 5

MS Kinsey matrix: 5

BCG matrix: 6

Comparison of Mickensey and BCG matrix: 7

Product Selection: 8

Tesla Attractiveness 8

Tesla strategy implementation: 13

Bowman strategy clock: 13

Generic strategy: 14

Cost leadership: 14

Differentiation leadership: 14

Roger model of Tesla Motors: 15

What is the selected market? 15

Our selected product: 15

What shall we win: 15

Sources required: 15

Management required: 15

Safe Framework for Tesla: 16

Alignment of goals and plan: 16

Built-in quality of Tesla cars: 16

Transparency: 17

Program execution methodology of tesla: 17

Product Leadership: 17

Conclusion: 18

Recommendation: 18

References 19


Research on Financial Analysis of Tesla
Research on Financial Analysis of Tesla



 Overview:

Tesla:

Tesla is a company incorporated in USA. The company was founded in the year of 2003 but after Elon musk joined the company, the potential of the company really started to show itself (Perez, 2020). Tesla business expects to sell about 23% of stock of its battery electric vehicles and 16% of stock of its plug-in hybrids globally by 2020.The true name tesla made in the market was by way of introduction of model S and Model X. Using these opportunities, tesla is investing in establishment of new factories around the world. This is also the best option for growth of tesla as more the future demand for electric vehicle is expected to rise.

Ansoff matrix:

The Ansoff matrix is used to determine which overarching strategy the company should utilise, as well as which marketing strategies should be implemented to properly aid with the overall strategy of the company (Leslie, 2022). Ansoff matrix is useful as it tells us about which strategic direction that we need to follow in order to successfully grow our business. Graphical representation of Ansoff matrix is given as:

There are four types of business strategies that are needed to be considered keeping in mind the fact that whether the criteria for such decision are met. 

Tesla Motors and Ansoff model:

Ansoff matrix provide us with four quadrants based upon different market conditions. In light of those market conditions, we can classify our company as an entity of four quadrant. This is because of the fact that our company is operating in the market of electrical vehicle that is relatively new and the product that the company is trying to sell is also relatively new.  (Dawes, 2018). This calls for diversification in the market and the product.

Diversification in the market is obtained by introducing many related but new products in the market. Because by doing do, the market customer base increase and the goodwill starts to build up. At the end, a point shall come when the market has now achieved total independence from other markets and is producing and selling products on its own. (Andrew, 2021).

Tesla can help in diversification of market by means of producing many related electric products. This was done by tesla in the form of introduction of its cyber truck. Along with its grand release hoisted by one and only Elon musk, cyber truck made everyone in the world turn their head towards the electrical market. (Yaqoob, 2021).

Matrix comparison:

The comparison between Mickens also known as the GE and BCG matrix is as follows.


The graphical representation of Mickensey matrix is given as:


Based on eh given criteria, our company lies on the cube where it occupies high amount of industry attractiveness that can is because of the market of electrical vehicle profitable. Also due to the past profits of the company along with eh amount of share that the company can raise, our company lies on the middle upper corner of the matrix. This mean that investment in the company shall be profitable decision (V.J.Thomasa, 2019). 


BCG matrix:

The specimen for BCG matrix for tesla is as follows:

BCG Matrix

High market share

Low market share

Research on Financial Analysis of Tesla
Research on Financial Analysis of Tesla


High growth rate

Featured-Star

Constant innovation

Cleaner energy production

Model 3

Question marks

Energy Storage

Solar energy

Miscellaneous accessories


Low growth rate

Cash cow

Sexy models like (Model S, Model X, Model Y)

Current best sellers along with

Power walls charger.

Deadweight-Dogs

Some manufactures complain about some issues with some models


BCG matrix put the condition of our company into four different quadrants. The explanation of these quadrants is given as

The star if our company is the continuous innovation along with the moto of cleaner production of cars. This shall help the company create market goodwill and as the market matures, this idea shall become a cash cow holding huge market share (FEEDOUGH, 2022).

Cash cow of tesla is the new model named model X and model S. These products have created a positive image of the company in the new market. And when the market ultimately matures, the company shall experience a cash cow phenomenon where the company will, due to economies of scale produce cars at less cost while the price remaining same hence profiting from its sale.

Dogs’ category shall be given to the solar electric cars. The company should not invest in this field as the market share along with market growth is slow. These products shall only create cost to the company and will be disadvantageous.

Question mark is the product of tesla named the wall charger. The market growth is high as the product will be helpful in a variety of ways but the market share is low. So, it creates a critical situation for the company as the product, on one hand can prove to be beneficial and on the other hand, due to public unacceptance, the product may flop.

Comparison of Mickensey and BCG matrix:

Mickensey matrix tells us about the decision that can be make on a specific product at a time like our model X and model S. These decisions are based upon several criteria like the geographical locations and the market potential. But BCG matrix takes into account all of its products at a time and evaluate about which one of those will be beneficial in the present or in the near future along with which one of those will create unwanted cost for the company. 

Mickensey matrix have 9 grids of decision making. But those can only e applied on a single product at a time While BCG matrix has four categories of products and it account for all the products at a same time.

Mc Kinsey matrix provide the user with the financial decision on a product based upon its factors While BCG matrix tells us about the nature of our products along with their potential but no decision-making guidance is given along with it.

Mc Kinsey matrix compare the maturity of market along with the maturity of our product where BCG tells us about the market growth rate along with market share. BCG comparison is better as the market maturity is unrelated to our company if the market share of our product is low.

The model that is most acceptable to tesla is the BCG matrix as it uses the variables of market strength and market share as a whole which is more accurate than the market and product maturity. It also takes into account all type of products at the same time so that proper comparison can be conducted of those products. 

In case of tesla, launching a new product as the Model 3 in this new market will result in the product being a star according to the BCG matrix along. Marketing these products shall require diversification, of suppliers and buyers according to the Ansoff matrix.

Product Selection:

Tesla has a number of products available to it that it can use for its strategy evaluation but we shall compare the model S. This shall be compared with the Volkswagen ID 4, a flagship electric cars series launched by Volkswagen. The following comparison is given as:

ID-4 has a much cheaper starting price at $40,760 as compared to Tesla’s model S that have a starting price of $94,990. This could result in Volkswagen to increase its market share by high amount of sales.

Tesla Model S is a total electric car having no available unit that consume any type of fuel whereas, Volkswagen’s ID-4 is semi electric and can also run-on fuel. This will increase the competitive strength of the company along with reducing the industry attractiveness as the industry is moving towards a green energy production plan that is against the use of fuel vehicles.

Tesla have sold 20,301 of its model-S in the year of 2020 while Volkswagen sold about 2755 of its units. This helps the company Volkswagen to increase its customer base and also in the diversification of its product in the market.

Tesla owns a huge overall market share in the electric industry. According to the financial data of the government, nearly 80% of all the electric vehicles registered in the US is a Tesla. This gives a window of maximum 20% market share to accommodate in for all of its competitors. In short, tesla is having a Monopoly on the market of electric vehicle.

Based on the analysis above, the company Tesla and Volkswagen seems to be equal of each other. But, due to the tesla vehicle being a total electric vehicle, the market attractiveness is high for the model S and the high profit margin on tesla vehicle is more than that of Volkswagen resulting in tesla ending up with more profits than Volkswagen. 

Tesla Attractiveness

To decide whether it would be profitable for the company to invest in its new model 

Research on Financial Analysis of Tesla
Research on Financial Analysis of Tesla



Industry Attractiveness








Business Unit 1

Business Unit 2



Factor

Weight

Rating

W. Score

Rating

W. Score


Industry Growth Rate

35

0.5

17.50

0.7

24.50


Industry size

13

1

13.00

0.3

3.90


Industry Profitability

27

0.9

24.30

1

2 7.00

Research on Financial Analysis of Tesla
Research on Financial Analysis of Tesla


Industry Structure

6

0.2

1.20

0.8

4.80


Trend of Prices

5

0.4

2.00

0.2

1.00


Market Segmentation

14

0.6

8.40

0.9

12.60









Total Score

100


66.40


73.80



COMPETITIVE STRENGTH




BUSINESS UNIT 01

BUSINESS UNIT 02


Factor

WEIGHT

Rate

W. Score

Rate

W. Score


Market Share

13

0.4

5.20

0.7

9.10


Relative Growth Rate

35

0.9

31.50

1.00

35.00


Company’s Profitability

41

1.00

41.00

0.3

12.30


Brand Value

7

0.5

3.50

0.1

0.70


VRIO Resources

2

1.00

2.00

0.4

2.00


CPM Score

2

0.4

0.80

0.9

0.80


Total Score

100


84.00


59.90





According to the financial data for tesla, the sector that is best suited for investment is the electrical car sector. This is because of the relative demand of tesla cars as compared to tesla battery. This can be easily explained through analysis of the financial statement of Tesla.





Tesla strategy implementation:

Tesla’s market attractiveness is at the all-time high in the past years. This is because of effective marketing and positive social image of Elon musk in the global market (Zhou, 2022). But, since the company has incorporated recently, the competitive strength of the industry is not at a stable level because of low amount of retained earnings as compared to its competitors. This result in the company to fall in the upper middle block of the mickensey matrix. This means that it is a good decision to invest in the industry and help in growth as the growth is probable to be profitable.

As for the Volvo, the competitive strength is high but the industry attractiveness has seen a dip in the previous years. This is because of the introduction of the electric vehicles in the market. That make the company fall in the middle-left block of the matrix. This means that it will be best for the industry to invest and grow as it has enough retained earnings to be competitive in the market.

Bowman strategy clock:

The Bowman Strategy Clock, often referred as Bowman's Business Plan, is a promotional strategy that allows a firm to analyse its position with respect to the offers of rivals. Cliff Bowman and David Faulkner, two analysts, formed it. Strategy Clock by Bowman illustrates how a company may position a product or its related service in the two dimensions (Khan, 2021). There's the price on one hand, and the perceived worth on the other. Examining various combinations of these two parameters within the Bowman Strategy Clock yields eight potential strategies grouped into four quadrants.

According to bowman strategy clock, our company, tesla is enjoying the price index of 6. That can be said as having risky high margin. The high margin Is because of the fact that our industry has little to no competition in the market of lithium-ion batteries and the electric cars. The products produced by tesla are termed as luxury items (Liang, 2022). This is because of the fact that our products have no other competitor in the market so, we can charge any type of amount for this. Tesla, if needed can even start a level 7 pricing that is known as monopoly pricing of its products. This is because Tesla have the highest market exposure then all of other companies that have entered the market and is aware of marketing strategy of these specific products.

In case of Volkswagen, the company has entered the market of electric cars and hence occupy the range of 4 to 5 in the bowman clock (Barabba, 2019). This amount is low because of the existence of tesla on the market and the number is higher than the Volvo because the market of electric cars is still yet empty.

Generic strategy:

Porter represented the generic strategies having 4 possible outcomes for our company. These are as follows:

Cost leadership.

Cost focus.

Differentiation leadership.

Differentiation focus.

Cost leadership:

The tesla company lies on the Cost leadership category, it is because of the fact that the company has a broad market competence along with distinction in cost leadership. This distinction is because of special research and development done by tesla in the field of battery and car production. The company has achieved ways of producing batteries of its vehicles in effective manner. The majority of the cost of an electrical vehicle comes from the cost of producing the lithium-ion battery (ThomasGreckhamera, 2021).

Differentiation leadership:

Tesla, in order to dominate the market, wanted one of a king individual to turn the corporation. For this reason, tesla appointed Zachary Kirkhorn as its CFO. Kirkhorn worked as a Senior Business Analyst for a well-known company named McKinsey & Company. He served in that company for for nearly three years. Kirkhorn reportedly joined the company Tesla as a Senior Analyst in the department of Finance in year 2010. He was then named as the Director of Finance in December of the year 2014, and ultimately the Vice President of Finance in December 2018. The Volkswagen has made its way to the market of electrical vehicles but, has not quite gotten a grip on understanding the market trends. It can, for now focus upon the cost focus strategy. According to this strategy, the company is to use its finances in researching better ways to reduce the cost of our selected product. Volkswagen because of long history of beneficial years of sales, has a large amount of retained earning avail that can be used to finance this research.

Roger model of Tesla Motors:

Rogers' innovation adoption curve is a model that separates innovators into groups based on the notion that some people are more adaptable than others. It's also known as Multi-Step Flow Theory or Diffusion of Innovations Theory. Innovators. Humans are naturally nice and imaginative, according to Rogers. They only become toxic when the value process is dominated by a negative self-concept or external limitations. Carl Rogers thought that being in a condition of congruence was necessary for self-actualization (poorghorban, 2021).

To account for this model, tesla needs to answer following questions:

What is the selected market?

Tesla has selected the market of electrical vehicles as the market is in its early development phases having little to no competitors. This decision was made taking into account the low amount of capital needed to enter and remain in the market. 

Our selected product:

Tesla selected a product that has the ability to run totally on electricity. Negating the use of fuel that all the other cars on road desperately need. This was done to not only bring innovation but to also attract the general public who is now inclined towards a green future.

What shall we win:

In case tesla is able to build its goodwill in the market. It can work as a monopolistic company at the time when market matures. This is because of the fact that it will be able to gain buyers trust in the market and hence the only thing remaining shall be to maintain that goodwill.

Sources required:

Since Tesla has entered the market where no research and development were conducted by previous companies, the chances for potential drawbacks and initial losses were high enough to be virtually certain. To cope with this, tesla need a hefty amount of capital to build its infrastructure from the start.

Management required:

Tesla, in order to survive the market, need a good management otherwise its story will end even before starting. To cope with this issue, tesla hired Zachary Kirkhorn who is an MBA from Harvard. He, along with the business giant Elon musk, both are responsible for leading tesla into a brighter future.

One competitor of tesla is Volkswagen. Volkswagen is one of the early majorities when it comes to the market of electric vehicles. The company has set foot on the market where the probability of competition is comparatively low as compared to the market of vehicles that has now been clustered by many manufacturers. This company can enjoy a better and profitable future in the market as by the time the market is mature, the company has produced a positive goodwill in the market.

SAF Framework for Tesla:

You may use the SAF Matrix to rank Tesla's strategic choices based on Suitability, Acceptability, and Feasibility (SAF). Analysing a SAF Matrix is another term for this process.


SAF Analysis recommends that the approach with the highest score is the best alternative.


Suitability

A strategy's suitability is evaluated in terms of its capacity to help your company achieve its objectives. It's a critical question to ask since, ultimately, you're developing a plan to achieve certain goals and objectives.

In Suitability, does the suggested strategy meet the organization's most important opportunities and threats?

When assessing someone for suitability, there are several factors to consider. Consider not just if the strategy will help you achieve your goals and objectives, but also whether it is appropriate for the Tesla culture, market, and capabilities.


Acceptability

Suitability for service in the SAF Stakeholders' perspectives are crucial to the analysis. Managing stakeholders is a crucial part of strategy since you want to make sure everyone is on the same page and on the same page only.

Acceptability, like suitability, is linked to Tesla's strategic aims. You need to know what returns you may expect from this approach, as well as how much risk there is that those gains won't materialise.

However, despite the fact that a company's return or risk may not be simply based on financial considerations, acceptability is generally linked to the data. The usage of frameworks like PESTLE Analysis and the Five Forces model, which focuses on profitability, may be an important first step in developing an acceptable strategy.


Feasibility

When it comes to strategic planning, feasibility is critical since it questions whether the firm has the ability to carry out the strategy. The resources, talents, timeliness, changes in the market, and financial resources may all be taken into account while doing this SAF Analysis.


Program execution methodology of tesla:

Program execution is at the heart of Tesla’s plan, and it drives everything else in the framework. Teams and projects must be able to provide high-quality, functioning software and commercial value on a regular basis. The tesla company have the quality and the quantity of a particular type of vehicle which, in return, creates an opening for IT firms in the state which have ample experience in human and human behaviour machine interfaces, still some of the tech giants CEO believes in the fact that the IT giants will be more focused on the plan that is mainly focused upon creating the software in machine learning future companies which are already available in the general market and there shall be no more barriers to entry . those barriers have fallen as producing high volumes of car at low cost is very different to limited production of luxury vehicles which Tesla is trying to achieve.

Product Leadership:

Product leadership is the one of the most Only leaders can modify the system and create the atmosphere required for all of the basic concepts to be adopted, hence SAFe demands a lean-agile leadership style. One of the major concern is the pressure on the CEOs of the established car companies as the median tenure of a company CEO is 5 years and the new strategic initiative will require 7 to 8 years and not having a strong quarterly results mean you can't afford the luxury of conducting that strategy initiative the successful new entrant will be the leadership holding large shares or who are privately owned such as Tesla as they can have a leverage to pursue the technology, business model,  markets trends while focusing on there long term strategy.

The Scaled Agile Framework's concepts are intended to benefit the entire firm by encouraging lean-agile decision making across functional and organisational boundaries. The principles are meant to influence not only leaders and managers' actions, but everyone in the organization's attitude toward lean-agile thinking, which includes methodologies like Lean Portfolio Management.

The competitors of Tesla like GM motors and Volkswagen have made an appropriate plan to infiltrate the market and take over the market that is still a new and immature market. Both the companies have the appropriate means of building the quality in the market because of efficient research and development department and a vast reserve of retained earnings. 

Conclusion:

Tesla has made a proper plan to make its business make its mark in the financial market of electrical vehicles. It has formed a proper plan and appointed qualified leadership to make its plan into a reality. The program has been appropriately executed and the resultant. In short when it comes to the market of electrical vehicles, tesla has the upper hand.

Recommendation:

Tesla should invest in research and development department to further reduce the cost along with increasing the efficiency of its batteries. This is because of the reason that the industry is going to be more crowded in the coming years. Competition from competitor products along with demand of competitive product is bound to increase in the coming future. To cope with this future crisis, tesla is establishing its factories in new countries availing the current governmental subsidies that can reduce the cost of expansion. This step is critical as the probabilities of further subsidies is low making the infiltration of other industries in the market much harder than before.


 

References

Andrew, J. ,. R. A., 2021. Field effectiveness of Volvo advanced driver assistance and headlighting systems. Accident Analysis & Prevention, 159(1).

Barabba, V., 2019. Assessing Volvo’ innovation strategy over three decades using the “Three Box Solution”. Strategy & Leadership, 47(2).

Chawla, N., 2021. A CASE STUDY ON VOLKSWAGEN PRINT AD THINK SMALL. PALARCH’S JOURNAL OF PALAEONTOLOGY AND EGYPTOLOGY, 18(17).

Chiu, C.-C., 2019. Rule-Based BCG Matrix for Product Portfolio Analysis. International Conference on Software Engineering, Artificial Intelligence,, 1(1), p. 17–32.

Dawes, J., 2018. The Ansoff Matrix: A Legendary Tool, But with Two Logical Problems. 1(1), p. 5.

GURCAYLILAR, 2018. APPLYING ANSOFF’S GROWTH STRATEGY MATRIX TO INNOVATION CLASSIFICATION. international Journal of Innovation Management, 22(4).

Khan, M. R., 2021. A critical analysis of Elon Musk’s leadership in Tesla motors. Journal of Global Entrepreneurship Research, 1(1).

Leslie, 2022. Analysis of the Field Effectiveness of Volvo Model Year 2013-2020 Advanced Driver Assistance System Features. Transportation Research Institute (UMTRI), 1(1).

Liang, J., 2022. A Case Study of Marketing at Tesla Based on the 4V Theory. Proceedings of the 2022 7th International Conference on Social Sciences and Economic Development, 1(1).

Perez, Y. C. &. Y., 2020. Business Model Design: Lessons Learned from Tesla Motors. Towards a Sustainable Economy , 1(1), p. 53–69.

poorghorban, M. R., 2021. Application of Hall-Roger model in measuring the profit margin of basic metals industry. Economic research, 5(16), pp. 25-34.

Putta, 2018. Benefits and Challenges of Adopting the Scaled Agile Framework (SAFe): Preliminary Results from a Multivocal Literature Review. International Conference on Product-Focused Software Process Improvement, 1(1), p. 334–351.

Putta, A., 2018. Benefits and Challenges of Adopting the Scaled Agile Framework (SAFe): Preliminary Results from a Multivocal Literature Review. International Conference on Product-Focused Software Process Improvement, 1(1), p. 334–351.

Razmi, J., 2020. An Assessment Model of McKinsey 7S Model-Based Framework for Knowledge Management Maturity in Agility Promotion. Journal of Information & Knowledge Management, 19(4).

ThomasGreckhamera, F. A., 2021. generic strategies: A set-theoretic configurational approach. Long Range Planning, 54(2).

V.J.Thomasa, E., 2019. Market entry strategies for electric vehicle start-ups in the automotive industry – Lessons from Tesla Motors. Journal of Cleaner Production, 235(1), pp. 653-663.

Yaqoob, K., 2021. The Role of Digital Marketing in Promoting Ansoff Matrix Strategies: A survey study in Al-Alamiah store in Mosul City. Muthanna Journal of Administrative and Economic Sciences, 11(3), pp. 241-255.

Zhou, Y., 2022. Proceedings of the 2022 7th International Conference on Social Sciences and Economic Development. Advances in Economics, Business and Management Research, 1(1).


Risk Management Research Article


Risk Management

 

Table of Content

Sr.

Title

Page No.1.

Executive Summary2.

Introduction 

Literature Review

Empirical Results

Conclusion 

References 

Appendix 

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Risk Management Research Analysis
Risk Management Research Analysis


 

Executive Summary

The danger of borrowers defaulting on their loan obligations is referred to as credit risk. A huge number of institutions have created sophisticated risk quantification, aggregation, and management systems and models in recent years. These models' outputs are also becoming more essential in risk management and performance monitoring processes at banks. We attempt to address the issue of short-term loan default prediction for a Tunisian commercial bank in this research. From 2003 to 2006, we used a database of 924 credit records given to Tunisian businesses by a Tunisian commercial bank. The findings of the K-Nearest Neighbor classifier technique show that the best information set is related to accrual and cash-flow, and the good classification rate is in the order of 88.63 percent (for k=3). To analyze the model's performance, a ROC curve is plotted. The AUC (Area Under Curve) criterion for the first model is 87.4 percent, 95 percent for the third model, and 95.6 percent for the best model incorporating cash flow information.

 

Introduction

The assessment of bank credit risk is frequently utilized by banks all around the world. Because credit risk assessment is so important, a variety of methods are employed to determine risk levels. Furthermore, one of the financial community's primary tasks is credit risk management (Serval, 2008). Credit risk is defined by the Basel Committee on Banking Supervision as the risk that a bank borrower or counterparty may fail to meet agreed-upon obligations. Clients are classified by their profile by banks. Customers' financial backgrounds and subjective criteria are assessed during the classification process. Financial ratios are crucial in determining risk levels (Berk et al., 2011). These are objective ratios that show the financial statement of a company. Financial documents such as the balance sheet, income statement, and cash flow statement are used to gather data for calculating objective financial ratios. There are numerous more subjective criteria, which are dependent on the bank's decision-making approach and mission (Berk et al., 2011).

In a consulting document, the Basel Committee on Banking Supervision attempted to provide guidance to banks and supervisors on appropriate credit risk assessment and valuation policies and practices for loans, regardless of the accounting methodology used. A bank's policies should correctly address validation of any internal credit risk assessment models, according to the third principle in this article. This principle's implementation turns out to be a daily decision based on a binary classification issue that distinguishes excellent payers from bad payers (Karaa & Krichene, 2012). Clearly, assessing insolvency plays a significant function, as a good evaluation of a borrower's quality can aid in deciding whether or not to issue the sought loans. The Basel Committee suggests using either an external mapping technique or an internal rating system to calculate credit risk capital needs (Karaa & Krichene, 2012).

Although the external mapping strategy is difficult to apply due to the lack of external rating grades, the internal rating approach is straightforward and straightforward to adopt, since many techniques for developing credit-risk assessment models have been proposed in the literature. Furthermore, the subprime mortgage crisis, which has rocked the United States and Europe and revealed the banking sector's vulnerability, has cast doubt on the accuracy and utility of agency ratings (Matoussi & Abdelmoula, 2009). Credit scoring methodologies, in reality, are used to assess both objective and subjective variables. In the 1950s, these methods became popular all across the world (Abramowicz et al., 2003). The collecting of client information is standardized using these ways. Furthermore, the scoring system is used to determine whether or not a loan should be approved. Traditional statistical techniques such as logistic regression (Steenackers & Goovaerts, 1989), multivariate discriminant analysis (MDA) (Altman, 1968), classification trees (Davis et al., 1992), neural network (NN) models (Desai et al., 1996, Matoussi & Abdelmoula, 2009; Karaa and Krichène, 2012), and nonparametric statistical models such as k-nearest neighbour (1997). Bayesian classification rules utilizing Nave Bayes classifiers have been proposed in recent contributions. The findings of these investigations showed that they can often outperform the most commonly used strategies. In this context, bankruptcy prediction models developed by Sarkar and Sriram (2001) and Sun and Shenoy (2007) were successful.

Risk Management Research Analysis
Risk Management Research Analysis


Literature Review

Theoretical Framework of Credit Risk Problem

The formulation of the optimal form of the lending contract is one of the most significant applications of agency theory to the lender-borrower dilemma. There is an information imbalance in the credit market between the borrower, who usually has better information about the investment project and its possible earnings and risks, and the lender (the bank), who does not have enough and reliable information about the investment project. This lack of quantity and quality information causes issues both before and after the transaction. Moral hazard and unfavorable selection are common when asymmetric information is present. A classic principal-agent dilemma can be seen in this scenario. According to the nature of information asymmetry, the principal-agent models of agency theory can be classified into three groups (Karel, 2006). To begin, we look for models that are classified as moral hazard because they have ex-post asymmetric knowledge. After signing the contract, the agent is given some confidential information. Moral hazard happens when an asymmetric information problem arises after a transaction has taken place. Because the borrower possesses information about the project that the lender does not, the lender runs the risk of the borrower engaging in activities that the lender does not want because they make it less likely that the loan will be repaid (Matoussi & Abdelmoula, 2009).

Second, we seek out unfavorable selection models, which have ex-ante asymmetric information (Karel, 2006). Before signing the contract, the agent in these models gets access to personal information. Adverse Selection occurs when a borrower has significant information about the quality of a project that the lender does not (or vice versa) before the transaction takes place. This occurs when the potential borrowers who are most likely to have a negative outcome (poor credit risks) are the most engaged in seeking a loan and hence are the most likely to be chosen. Because the riskiness of projects is unknown, lenders' pricing cannot distinguish between good and bad borrowers in the basic situation. Finally, there is signaling, which is the third class.

This issue has usually been studied within the context of costly state verification, which was first introduced by (Townsend, 1979). The agent, who has no endowment, borrows money from the principal to fund a one-time investment project. A moral hazard issue confronts the agent. Should he report the true value or should he reduce the project's outcome? Ex-post moral hazard is demonstrated in this instance. Furthermore, we may encounter an ex-ante moral hazard situation, in which an agent's unobservable effort during the project implementation may have an impact on the project's outcome. According to Townsend (1979), the best contract for solving this problem is the ordinary (or simple) debt contract. The face value of this conventional debt contract is the amount that the agent must repay once the project is completed. (Diamond, 1984) proposed another theoretical basis for simple debt contracts, in which a costly punishment replaced the costly state verification. Only under the risk neutrality assumption, according to Hellwig (2000, 2001), are the two models comparable. When risk aversion is introduced, the costly state verification model continues to work, but the costly punishment model fails. Credit institutions can deal with the asymmetric information problem and its repercussions on credit risk appraisal in the real world by using either guarantee (collateral) or bankruptcy prediction modelling, or both (Karaa & Krichène, 2012).

Risk Management Research Analysis
Risk Management Research Analysis


Credit Risk Assessment and Bankruptcy Prediction

Following a rash of high-profile bank failures in Asia, regulators have recognized the need for advanced technology to assess credit risk in bank portfolios and have urged banks to use it. Correctly assessing credit risk also allows banks to engineer future lending transactions to meet specific return/risk criteria. Credit risk analysis necessitates the development of reasonably accurate quantitative prediction models that can serve as early warning signals for counterparty defaults. In the literature, a number of researchers presented two primary approaches to credit scoring. The first approach, known as structural or market-based models, was proposed by (Merton, 1974), and is based on modelling the underlying dynamics of interest rates and business characteristics to derive the default probability derivation. Initially, this method is based on the asset value model, which has an endogenous default process and is linked to the firm's capital structure. When the value of a company's assets falls below a certain threshold, it is said to have defaulted (Crouhy et al., 2000).

The second method relies on empirical or accounting-based models, in which the relationship between default likelihood and business characteristics is discovered from data rather than modelled. Some strategies in this area were synthesized by Raymond (2007), Thomas et al. (2002), Galindo and Tamayo (2000). Academics and practitioners have researched bankruptcy prediction extensively, as evidenced by the studies of Beaver (1966) and Altman (1968). There have been several models created and empirically tested. Altman's well-known Z-Score (Altman, 1968) is a linear discriminant analysis model that was used to forecast the likelihood of a corporation defaulting. The Ohlson O-Score (Ohlson, 1980) is based on generalized linear models or multiple logistic regression, which have been used to determine the best predictors of bankruptcy and the prediction accuracy rate of their occurrence. In order to anticipate bankruptcy, neural network models were tweaked and employed (Atya, 2001; Matoussi & Abdelmoula, 2009). With the capacity to incorporate a large number of features in an adaptive nonlinear model, their strong predictive capability makes them a popular choice (Kay & Titterington, 2000).

Many studies have concentrated on non-parametric approaches (e.g., k-nearest neighbour) (Henley & Hand, 1996), decision trees (Quinlan, 1992), and neural networks (Mcculloch & Pitts, 1943). Other classification systems, such as Support Vector Machine, combine various techniques to construct a classification model (e.g., Lee and Chen, 2005; Lee et al., 2002). West (2000) compared the credit scoring accuracy of five Artificial Neural Network models, including multilayer perceptron, radial basis function, fuzzy adaptive resonance, mixture-of-experts, and learning vector quantization. West (2000) employed two real-world data sets, one Australian and the other German, in his research. In order to improve his predictive power, he used tenfold cross validation. Both good and terrible credit rates were mentioned. Finally, he compared the results to those of five other common techniques: linear discriminant analysis, logistic regression, k closest neighbour, kernel density estimation, and decision trees. The findings suggest that multilayer perception may not be the most accurate Artificial Neural Network model, and that credit scoring applications should include both combination-of-experts and radial basis function Neural Network models. In the typical situation, logistic regression is a more accurate and precise method than Neural Network models when compared to older methods.

Despite extensive research into credit scoring, Vera et al. (2012) claim that no consensus exists on the best classification technique to utilize. When comparing the outcomes of different investigations, Baesens et al. (2003b) discovered that certain disputes can occur. However, according to Thomas et al. (2002), most credit scoring techniques function similarly. Indeed, the interpretability and openness of certain procedures may lead banks and financial organizations to choose them (Martens et al., 2009). According to Vera et al. (2002), the predictive performance of credit scoring algorithms, as well as the insights or interpretations offered by the model, are both very essential.

Risk Management Research Analysis
Risk Management Research Analysis


Empirical Research Design

Banks operate in a highly competitive market, so the quality of service provided during credit risk assessment is critical. To obtain a competitive edge, when a customer requests credit from a bank, the bank should examine the request as quickly as feasible (Berk et al., 2011). Furthermore, the same process is repeated for each credit demand, which costs the bank money. Because of the importance of credit risk analysis, financial institutions have created a variety of approaches and models to help them decide whether or not to extend credit (inko, 2006).

Parametric and non-parametric problems are the two types of classification methods. In fact, parametric techniques solve problems by estimating the parameters of distributions based on the assumptions of regularly distributed populations (Zhang et al., 2007). Non-parametric approaches, on the other hand, do not make assumptions about the individual distributions involved, and thus are distribution-free, according to Berry and Linoff (1997). A non-parametric statistical method is exemplified by the k-nearest neighbour classifier. A K-NN classifier searches the pattern space for k training (Pranab & Radha, 2013) instances that are comparable to unknown cases when given an unknown case. The K-nearest neighbours of the unknown cases are the k training cases (Ravinder & Aggarwal, 2011). When the dependent variable has multiple values, such as high risk, medium risk, and low risk, the K-NN classifier can be effective. Furthermore, for optimal performance, the K-NN classifier needs an equal number of good and bad sample examples (Hand & Henley, 1997). The k-NN algorithm's performance is also affected by the choice of k, according to Berry and Linoff (1997). This is something that can be tested. To assess the classifier's error rate, we utilize a test case starting with k=1. Each time, k is increased to accommodate one more neighbour, and the procedure is repeated. It is possible to choose the K-value that yields the lowest mistake rate. The larger the number of training samples, the higher the value of k.

ROC Curve as Performance Classifier

Receiver Operating Characteristics (ROC) is a performance graphing method that is widely used. In other words, a ROC graph is a visual, organizational, and selection approach for classifiers based on their performance. Fawcett is a fictional character who appears in the television series (2006). ROC graphs were first used in machine learning by Spackman (1989). He showed how ROC curves might be used to evaluate and compare algorithms (Fawcett, 2006). ROC graphs have become increasingly popular in the machine learning community in recent years. Because basic classification accuracy is typically an inadequate criterion for assessing performance (Provost & Fawcett, 1997; Provost et al., 1998). They also have characteristics that make them particularly beneficial in areas with skewed class distribution and disproportionate classification mistake costs (Fawcett, 2006).


Risk Management Research Analysis
Risk Management Research Analysis


The performance of a classifier is represented by a ROC curve, which is a two-dimensional graph. To compare classifiers, Fawcett (2006) recommends reducing ROC performance to a single scalar value that represents predicted performance. Many scholars, including Bradley (1997) and Hanley and McNeil (1982), propose calculating the area under the ROC curve, also known as AUC. The AUC is a percentage of the unit square's area, with a value that is always between 0 and 1.0. However, no realistic classifier should have an AUC smaller than 0.5, because random guessing yields the diagonal line between (0, 0) and (1, 1), which has an area of 0.5. (Fawcett, 2006).

Empirical Results

We created three different K-NN classifiers in our experiment. Data on financial ratios is used by the first classifier (cash-flows excluded). Non-cash-flow model will be used to describe it. The second model incorporates information from all ratio indicators (cash-flows included, collateral excluded). The cash-flow model will be used. All of the study's indicators are used in the third model. 'Full information model' will be the term used. The k-nearest neighbour (k-NN) methodology, according to Rafiul et al. (2008), is a simple and intuitively appealing strategy for dealing with classification difficulties because of its interpretable nature. Choosing an acceptable distance function for k-NN, on the other hand, can be difficult, and a poor choice can make the classifier extremely susceptible to data noise. We experimented with a variety of k values in our research (2, 3, 4 and 5). Based on our testing, we determined the best value of k for k-NN, which resulted in the best classification performance, which is presented in the result tables.

k-NN classifier with variation of the parameter k=2


Risk Management Research Analysis
Risk Management Research Analysis


Healthy 

Risky 


Healthy companies

358

100


Risky companies

100

366


% Total Good and Bad Classification


Good classification

78.35%


Bad classification

21.64%



Results for Cash-Flow models


K=2


K=3


K=4


K=5




Healthy

Risky

Healthy

Risky

Healthy

Risky

Healthy

Risky


Healthy

395

63

409

49

387

71

375

83


companies










Risky

59

407

56

410

72

394

92

374


companies










% Total Good and Bad Classification


Good

86.79%

88.63%

84.52%

81.06%


classification






Bad

13.20%

11.37%

15.48%

19.94%


classification








Results for full information models


K=2


K=3


K=4


K=5




Healthy

Risky

Healthy

Risky

Healthy

Risky

Healthy

Risky


Healthy

companies

393

65

406

52

381

77

383

75


Risky

companies

69

397

69

397

99

367

113

353


% Total Good a Good classification

nd Bad Classification

85.5%


86.90%


80.95%


79.65%


Bad

classification

14.50%

13.10%

19.05%

20.35%



The categorization results for both cash flow and complete information models. The classifiers with the best performance are bolded and highlighted in red. Tables 4 and 5 demonstrate that the best model with the best classification rate is the one combining accrual and cash flow data (table 4) with an excellent classification rate of 88.63 percent vs 86.90 percent for the third model with all data. Remember that our goal is to determine the new point's class label. The behaviour of the algorithm varies depending on k, and the value of K in this study was chosen. We can also come to the conclusion that k –NN is the best parameter. The findings were improved by increasing the parameter k to three. The percentage of correctly classified items is improving. Furthermore, when cash flow information is included, the model reduces error type I from 16.73 percent to 12 percent and error type II from 20.52 percent to 10.69 percent.

Criterion of the type I and II error


ERROR

K=2

K=3

K=4

K=5


NON-CASH FLOW MODEL

Type I

21.83%

16.73%

27.51%

27.25%



Type II

21.45%

20.52%

26.18%

27.72%


CASH FLOW MODEL

Type I

12.66%

12.01%

15.45%

19.74%



Type II

13.75%

10.69%

15.50%

18.12%


FULL INFORMATION MODEL

Type I

14.8%

14.80%

21.24%

24.24%



We want to estimate credit risk in this study by employing a set of financial ratios that are commonly used in loan contracts. The predictions on financial ratio selection show the link between financial ratios and credit risk. In the practitioner and academic literature, this evidence is well-known (Demerjian, 2007). Indeed, textbooks emphasize the importance of ratios in evaluating credit quality (Lundholm & Sloan, 2004), and academic research find that when employed as covenants, financial ratios give indications regarding borrower credit risk (Smith & Warner, 1979; Dichev & Skinner, 2002).

The ROC Curve

A perfect classifier's ROC curve is the curve that follows the two axes and orders all bad cases before good cases. For a given value of the sill, it would classify 100% bad situations as class bad and 0% good ones as class bad. A classifier with a ROC curve that follows the 45° line is ineffective, according to Yang (2002). At each value of the threshold, the same proportion of bad and good cases would be assigned to the class bad; the classes would not be separated. Between these two extremes, real-world classifiers develop ROC curves. The Area Under the ROC Curve (abbreviated as AUC) is a metric that can be used to assess the curve's performance (Hand, 1997). The curve with a higher AUC is preferable than the one with a lower AUC. We can see that the AUC requirement for the best model is around 95.6 percent (cash flow model). This is a good score because it is greater than 50%. This result backs with the preceding section's finding of a high categorization rate. We might conclude that cash flow data is a useful signal for bankers assessing lending applicants.




Conclusion

Client-loaning commercial banks require consistent models that can accurately detect and anticipate defaults. In the current competitive and unpredictable economic environment, Moonasar (2007) stressed that reducing credit risk is one of the most important issues that each bank must deal with. To assess a credit applicant's creditworthiness, we traditionally use scoring systems. In truth, credit scoring is a mathematical tool for assessing creditworthiness (Yang, 2002). Credit scoring is a classification process that divides borrowers into risk groups. Scoring systems are used to estimate the likelihood of a borrower or counterparty defaulting (Komor'ad, 2002). Credit scoring is crucial in assessing credit risk. According to Moonasar (2007), a popular way to credit scoring is to use a classification algorithm using data from prior customers to identify a link between the customers' qualities and their likelihood of defaulting on their debt. Credit scoring systems are used by lenders to evaluate whether applicants are likely to repay their debts. To distinguish between possible good and bad credit applicants, an accurate classifier is required. The best K-NN with k=3 for the three models, and the best global classification rate is in the order of 88.63 percent, according to the primary results. Additionally, a ROC curve is shown to evaluate the model's performance. The AUC requirement is 95.6 percent, according to the results. Our research, on the other hand, is lacking in that it.

 

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Appendix

APPENDIX 1: NON-CASH FLOW MODEL

Panel 1: K-NN with k=2


Panel 2: K-NN with k=3

Panel 3: K-NN with k=4

Innovation to FinTech Company


Innovation to FinTech Company

 

Table of Content

Sr.

Title

Page No.

Executive Summary

Introduction 

Background 

Evaluation 

Results & Discussion

Conclusion 

References 

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Innovation to FinTech research analysis
Innovation to FinTech research analysis


 

Executive Summary

Not only in rich economies, but also in emerging nations, there is increased competition between banks and fintech firms. However, it has yet to be noticed to the same degree in Latvia. The goal of this study is to compare Latvia's fintech development to that of Europe. The study examines the advantages and disadvantages of fintech services versus traditional financial sector services (banks, insurance companies, asset management and investment institutions, and so on), as well as consumer readiness to use fintech services. This study presents the findings of a survey aimed at determining how well-informed Latvian customers are about fintech services, their convenience, speed, and security, as well as how well-informed consumers are about other financial services. This study's hypothesis is that Latvian society is not yet ready to adopt fintech services, preferring instead banking services. The survey results suggest that respondents are typically uninformed of fintech services in Latvia, as well as the innovations and new financial products that go along with them. This study offers a number of recommendations to fintech entrepreneurs, start-up associations, risk capital funds, and government agencies.

 

Innovation to FinTech research analysis

Introduction

Fintech is the use of new technological breakthroughs to financial products and services. Our expert will explain what it is and how to utilize it. Fintech is a combination of the words "finance" and "technology" that refers to any company that employs technology to improve or automate financial services and operations. The phrase refers to a fast-expanding industry that helps consumers and businesses in a variety of ways. Fintech offers a seemingly limitless number of uses, from mobile banking and insurance to cryptocurrency and investment apps. The industry is enormous, and it will continue to grow in the next years. "41 VC-backed fintech unicorns worth a combined $154.1 billion," according to CB Insights. Many traditional banks are proponents and users of the technology, aggressively investing in, acquiring, or collaborating with fintech companies because it is easier to provide digitally savvy clients with what they want while also propelling the industry forward and being relevant. Fintech firms use technology to improve the security, speed, and efficiency of traditional financial services. Fintech is one of the fastest-growing tech areas, with companies developing solutions in practically every aspect of finance, from payments and loans to credit scoring and stock trading.[1],[2],[3]

1.1 What is the mechanism behind fintech?

Fintech is not a new industry; it has simply evolved at a breakneck pace. Credit cards, ATMs, electronic trading floors, personal finance apps, and high-frequency trading have all been a part of the financial sector in some manner since the 1950s. Financial technology's inner workings differ from project to project and application to application. Machine learning algorithms, blockchain, and data science are being used to accomplish everything from process credit risks to run hedge funds in some of the most recent breakthroughs. In reality, a type of regulatory technology known as regtech has emerged to help businesses like fintech negotiate the complicated world of compliance and regulatory challenges. Concerns about cybersecurity in the fintech business have developed as the industry has evolved. The global expansion of fintech companies and markets has exposed vulnerabilities in fintech infrastructure, making it a target for cybercriminals. Fortunately, technology is always improving, allowing fraud risks to be reduced and new dangers to be mitigated. Traditional firms and banks are continuously adopting fintech services for their own objectives, despite the industry conjuring up ideas of startups and industry-changing technologies. Here's a quick look at how the financial services business is both disrupting and improving some aspects of the industry.[4]

1.1.1 Banking

The fintech industry is dominated by mobile banking. Consumers have sought simple digital access to their bank accounts, particularly on mobile devices, in the field of personal finance. With the rise of digital-first banks, or Neo banks, almost all large banks now have some sort of mobile banking capability. Neo banks are virtual banks that offer checking, savings, payment, and lending services to customers through a mobile and digital infrastructure. Chime, Simple, and Varo are three Neo banks to consider.[5]

1.1.2 Crypto Currency & Blockchain

The emergence of cryptocurrencies and blockchain is running parallel to fintech. Blockchain is the technology that enables bitcoin mining and marketplaces, and both blockchain and fintech are responsible for improvements in cryptocurrency technology. Though blockchain and cryptocurrency are distinct technologies that might be considered outside of the world of fintech, both are theoretically required to develop practical applications that advance fintech. Gemini, Spring Labs, and Circle are some of the most essential blockchain companies to know, while Coinbase and SALT are examples of cryptocurrency companies.[6]

1.1.3 Investment & Savings

In recent years, the number of investing and savings apps has exploded as a result of fintech. Companies such as Robinhood, Stash, and Acorns are removing more hurdles to investing than ever before. While the approaches of these applications vary, they all use a combination of savings and automated small-dollar investing ways to expose people to the markets, such as fast round-up contributions on purchases.[7]

1.1.4 Machine Learning & Trading

The Holy Grail of finance is being able to forecast market direction. Machine learning has become increasingly crucial in fintech, with billions of dollars to be made. The strength of this AI subset rests in its capacity to process huge volumes of data using algorithms designed to recognize trends and dangers, giving consumers, businesses, banks, and other organizations a better knowledge of investment and purchasing risks earlier in the process.[8]

1.1.5 Payments

Fintech is particularly adept at moving money around. "I'll Venmo you," rather than "I'll pay you later," is now a common phrase. Venmo is a popular mobile payment service. Payment processors have revolutionized the way we do business. Sending money digitally over the world is now easier than ever. Zelle, Paypal, Stripe, and Square, in addition to Venmo, are popular payment businesses.[9]

1.1.6 Lending

Fintech is also revolutionizing credit by reducing risk assessment, accelerating approval processes, and simplifying access. Millions of individuals around the world can now apply for a loan using their mobile devices, and new data points and risk modelling skills are allowing credit to be extended to previously underserved groups. Furthermore, individuals can request credit reports numerous times a year without affecting their credit score, making the entire financing environment more open for all. Tala, Petal, and Credit Karma are notable credit firms.[10]

1.1.7 Insurance

While insurtech is rapidly becoming into its own business, it remains part of the fintech umbrella. Insurance is a late adopter of technology, therefore many fintech businesses are teaming up with traditional insurers to assist automate operations and broaden coverage. The insurance business is seeing a lot of innovation, from mobile vehicle insurance to health insurance wearables. Oscar Health, Root Insurance, and Policy Genius are a few insurtech startups to keep an eye on.[11]

 

Background

Fintech, or financial technology, is a phrase that describes companies that provide current financial technology. Since 2010, such businesses have become popular. Fintech companies are typically micro, small, or medium-sized businesses with a clear vision for introducing new or improving existing financial services. Fintech start-ups are common, and the number of them is growing all the time. Fintech startups are typically financed through venture capital and crowdfunding. Fintech startups, according to some experts, boost the financial system's efficiency (Vlasov, 2017; Vovchenko et al., 2017; Setyawati et al., 2017). Fintech companies have grown in popularity for two reasons. First, the global financial crisis of 2008 exposed the flaws in the old banking system that contributed to the disaster. Second, new technologies have enabled to deliver financial services with greater mobility, simplicity of use (visualisation of data), speed, and lower cost (Anikina et al., 2016).[07], [10]

The potential market for fintech service users is enormous, encompassing virtually the whole adult population of the world. According to the McKinsey Social Sector page (Chaia et al., 2010), a study conducted in 2010, nearly 2.2 billion financially unserved adults live in Africa, Asia, Latin America, and the Middle East, including 8% of the population of high-income OECD countries (60 million adults), 65 percent of the population of Latin America (250 million adults), 49 percent of the population of Central Asia and Eastern Europe (193 million adults), and 67 percent of the population in the Middle East. These are folks who could benefit from fintech services. The growing number of people throughout the world who are unable or unable to use traditional banking services adds to the development of FinTech, which provides similar services while being faster, cheaper, and more profitable than banks. These changes will increase operational and long-term risks for banks (Novokreshchenova et al.,2016; Fetai, 2105; Thalassinos et al., 2015). Sharf (2016), on the other hand, reports that a survey of 10,131 people in Australia, Canada, Hong Kong, Singapore, the United Kingdom, and the United States about their use of fintech products revealed that only 15.5 percent of all respondents used nonbanking services, with the number expected to rapidly rise in the future. Non-banking services are used often by 25% of respondents, who utilize 2-3 non-banking goods on a regular basis. These figures suggest that bank customers are also potential fintech customers.[12]

Evaluation

Fintech is one of the fastest expanding sectors of the economy, according to research by Accenture (a worldwide management consulting, technology services, and outsourcing firm). Investments in the business have rapidly expanded, reaching $12 billion in 2014 from $930 million in 2008. Europe saw the most significant growth (Accenture, 2015). For the years 2014 to 2016, the table presents data on fintech investment in the United States, Europe, and Asia.


Region

2014

2015

2016


USA

14.1

27.4

13.5


Europe

12.0

10.9

2.2


Asia

3.3

8.4

8.6



Table shows that in 2015, these regions received $46.7 billion in fintech investment. It dropped to $24.3 billion in 2016, although this does not indicate a drop in interest in this subject in general. Despite the increase in total investment in fintech, these companies are still unable to compete seriously with the banking and insurance sectors of financial services - according to a survey of young entrepreneurs, users of banking services in Latvia (2016-2017), the majority of clients are not ready to replace them with fintech alternatives (Kims, 2017).[13][14]

As we all know, technology, or more specifically, information technology, is the main driving force of business in a variety of industries. Mobile payments, data analysis, crowd-sourced platforms, and cryptocurrency are all supported by technology. Fintech is an industry that improves the efficiency of the financial system by using IT technology centered on cellular phones/smartphones. According to Gomber et al., conventional business drivers of the financial services industry are being combined with Internet-related technology. FinTech refers to a set of technologies that have grown more important in the management of financial transactions.

Fintech ecosystem organizations will become strong business drivers. Organizations active in the Fintech industry, according to Alt et al., include:

External Organizations (e.g., Financial Services Authority, Government Organization) are organizations that function as regulators.

Startups, Fintech Companies, IT Companies, and Telecommunication Companies are examples of network organizations, which are directly involved in the Fintech business network.

A corporation or organisation that uses Fintech services in its business dealings is referred to as an Internal Organization.

Financial Institutions, Regulators, IT Companies, Startups, Accelerators, Consulting Companies, Governmental Organizations, Retailers, and Telecommunication Companies are the organizations referred to as business drivers in Fintech in the study of Zavolokina, et al. FinTech service businesses are IT-enabled sources of information, service firms, or financial platforms. FinTech firms are now referred to either freshly founded FinTech companies or established IT companies that enter the financial industry domain. Individuals spend money to receive goods or services created by the firm, while companies pay wages in exchange for their labor or services. Money flow is also a key corporate factor. Money flow is the amount of money thrown into financial industry sectors to promote their development. Payments (payments), insurance (Digital Insurance), planning (Financial Planning), deposit & lending (Peer to Peer Lending), Crowdfunding, Blockchain, Capital Raising and Investment Management, Data and Analytic, and Security are the eight categories that FinTech services based on money flow are divided into. Technology has created new business models in E-Commerce, such as online money flow.

Fintech mechanisms include the creation of new services/products/business processes, as well as the improvement of existing services/products/processes to improve consumer value or make them more transparent, accessible, and cost-effective. The utilization of technology breakthroughs supports these operations, as evidenced by the element of "application of IT to finance." FinTech's disruptive function is defined as the development of alternatives to traditional banking services, such as the replacement of the bank as an intermediary. Finally, FinTech fosters rivalry not only among service entrepreneurs, but also among banks.

In the financial services industry, technological innovation is a business innovation that relies on IT. Business requires a lot of creativity. The word innovation, according to C. Lin, comes from the Latin word innovate, which means "to create something new." Innovation is a crucial source of competitive advantage that can be sustained. Fintech companies provide financial services through cutting-edge technology. "Use of new technological and administrative expertise to offer a new product or service to customers," according to Fadilah et al. In such a competitive environment as the contemporary economic, social, and political globe, technical innovation is a critical factor in the creation of new kinds of value. New company models have been impacted by technological innovation and digitization.

3.1 Theoretical Framework

This study creates a theoretical basis for financial technology. FinTech is a new paradigm for business and technology innovation in the financial industry. This conceptual framework will serve as a practical guidance in field practice as well as a theoretical foundation for future research. In addition, the conceptual framework in this study is built on the description of Grand Theory, Middle Theory, and Applied Theory to generate constructs and dimensions. Many people nowadays believe that Modern Monetary Theory underpins the FinTech dilemma. Griffin claims that Modern Monetary Theory teaches things like Electronic Money and Monetary Policy, as well as the money supply and money flow speed. Another part of his idea concerned the economic impact of electronic money movements. The middle theory evolved from the next Grand theory. The Resource-Based View (RBV) Theory is the first middle theory to be employed. The essence of Resource-Based View (RBV) theory, according to Wernerfelt, is that organizations can obtain and maintain competitive advantages by building and exploiting important resources and skills.


Technology and resources are physical assets, according to Melville et al., and ability in managing organizations is an intangible asset, according to Barney. Roger's Diffusion of Innovations Theory is a component of Management and Technology Theory. The diffusion of innovation theory describes how to develop, modify, and improve technological uptake. Furthermore, firms that apply IT to finance create financial technology competition (FinTech). The organization's ability to accept an innovation quickly is critical. Competitive theory aids in the description of market development, new products/services, and new business models.

Business Drivers is the first construct created by combining numerous theories. Technology, organisation, and money flows are all important business drivers in the banking industry. Fintech is a type of financial service innovation that has recently gained popularity as a means of facilitating business, particularly in the financial services industry. The second construct is made up of dimensions like develop/change/improve, disrupt, apply IT to finance, and create competition. Finally, due to the existence of a Technological Innovation construct with a market development dimension as a result of a new product or service as a result of new processes and business models.

Results & Discussion

A poll of Indonesians was undertaken for this study to learn about their attitudes toward Fintech. There are 154 people who responded, with students accounting for 46.1 percent, freelancers for 5.3 percent, private sector workers for 30.3 percent, and government employees for 5.3 percent (18.4 percent). The respondents were between the ages of 17 and 55. A total of 62 percent of FinTech users are between the ages of 20 and 30. According to the poll results, 53.2 percent of them are very knowledgeable with FinTech. Many definitions exist for the term "fintech." Fintech is said to be a combination of "financial" and "information technology," according to some scholars. Based on the results of a research survey conducted by Zavolokina et al. in different countries around the world, as Research-1, and the results of this research survey, as Research-2, concerning people's perceptions on the meaning of FinTech in Indonesia, as indicated in Table.

No.

Meaning of Fintech

Research-1

Research-2


1

Application IT to Finance

35.7 %

85.7 %


2

StartUps

25.0%

2.6%


3

Financial Services

21.4%

7.8%


4

Technologies

17.9%

3.9%



Fintech is defined as IT applications to finance, according to the findings of these two study polls. As a result of the findings of this study, FinTech will develop numerous technological advances in the financial business sector as an IT application for finance based on its mechanism.

No

Function of Fintech

Research- 1

Research- 2


1

Apply and combine IT to Finance

35.5 %

63.6%


2

Disrupt

25.8%

2.6%


3

Create/Change/improve service

22.6%

28.6%


4

Create competition

16.1%

5.2%



The two research polls revealed the public's perspective of Fintech's role. As a consequence of the findings of the two studies, many people believe that Fintech's primary purpose is to apply and integrate technology into the financial business.

4.1 Critical Review & Analysis

The results of the Fintech conceptual framework are based on a careful assessment and analysis of the literature as well as the foundations of diverse theories. Business drivers are an input, Fintech mechanisms are an output, and Technological Innovation is an input.



4.1.1 Business Drivers

Business drivers that disrupt, apply IT to finance, and promote competitiveness for technology innovation in financial industry sectors. According to the findings of the poll, technology is one of the most important business drivers (68.8 percent). Money flow came in second (20.8 percent) and was followed by the organisation (10.4 percent) in driving business in the banking sector. In keeping with past Fintech research that emphasize the role of technology in conducting business.



4.1.2 FinTech Mechanism

FinTech mechanisms can help businesses grow, evolve, and improve. FinTech also causes financial industry disruption. Additionally, implementing IT for finance can result in increased business competition. Fintech disrupts (1.9 percent), implements IT in finance (67.9%), and creates corporate competitiveness, according to survey data (5.2 percent).


4.1.3 Technological Innovation

FinTech is a technology advancement that results from financial sector business innovation, resulting in new services/products, procedures, and business models. According to the findings of the survey, Fintech innovation resulted in 37.6% new services and products. In addition, 33.8 percent of FinTech creates new business models, according to the survey data. Finally, 28.6% of technology innovation results in a new financial business procedure.


Conclusion

The goal of this study is to create a conceptual framework for presenting the FinTech mechanism's involvement in technical innovation in the financial services industry. The findings of this study show that defining FinTech as an IT application for finance based on its mechanism will result in a wide range of technical breakthroughs in the financial services industry. This study's conceptual framework for FinTech was built on various underlying theories and corroborated the survey's findings. The main constructs in the FinTech idea are the Business Driver, Fintech Mechanism, and Technological Innovation. Finally, the FinTech conceptual framework developed as a result of this research will aid future Fintech development for practitioners and scholars. FinTech's conceptual framework can be tested in the future through actual research.

 

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