How can AI facilitate fintech algorithms to manage your finances?
“Beware of little expenses. A small leak can sink a great ship”- Benjamin Franklin
According to a 2019 survey by GoBanking Rules it was identified that approximately 70 % of Americans have less than $1000 stashed in their savings. The poll also revealed that even 45 % of them have no financial savings or securities at all. These numbers are reflected from a survey when the United States had a strong economy, and it is probable to increment due to COVID-19 pandemic due to notable high unemployment rates.
The coherent emerging trend of Artificial Intelligence (AI) and understanding on financial sector can create opportunities for individuals struggling to optimize their monetary goal. Machine Learning technology can identify the loopholes and create awareness on maximizing one’s personal wealth. Effectively managing one’s income and expenses is the first step towards financial freedom. Unfortunately most individuals or even corporate companies struggle to optimize their wealth management and often declare bankruptcy during times of financial crisis. Technology has realized this emerging demand and through efficiently designed algorithm it provides scope for individuals and corporations to improve their financial activities.
As there is a common saying “Beware of little expenses. A small leak can sink a great ship”- Benjamin Franklin.
What is Fintech?
Fintech is an emerging collaborative industry between technology and financial sector. The primary goal is to innovate and improve traditional financial methods. It has application in insurance, banking, trading stocks and even risk analysis and management for corporate projects. One of the most popular fintech application is mobile banking and payment . This option allows individuals to make financial transactions at increased convenience and speed. It is considered the third most used mobile application by individuals.
According to statista.com, there are approximately 8775 fintech startups in North America as of February 2020. This is an approximate 52% increment from 2019. Technology and finance are the most sought after fields and hence a collaboration between the two can create new opportunities. Some of the factors that play primary role in its adoption in geographical sectors include internet availability and the trust on its cybersecurity offered by the fintech company.
The various categories of Fintech companies include: Personal finance, Wall Street, Investment, Lending, Real Estate, Crypto and blockchain and Payment options.
The pie chart shows the funding distribution across various categories of the Fintech companies. The highest is mostly biased towards payment options and crypto currency being least. The reason why crypto is lacking behind in accumulating fund is because (i) it is a relatively new concept,(ii) the price volatility (iii) barriers of government regulation for freedom in penetrating in large scale into mainstream transactions. The payment specialized fintech companies design algorithm for regulating cash flow analytics. Real estate fintech platforms offers opportunity as an automated lending platform to identify accredited investors of real estate. Investing fintech application offers rob-advisors in asset management by analyzing individual client financial goal and risk tolerance.
The role of AI in Fintech
AI algorithm can play predictive roles to improve asset management among individuals or large corporations. It can keep track of notable and recurring expenditure that are responsible for increasing client’s debt portfolio. Financial data management is crucial to predict the future of the economy. AI algorithm can process these large financial data through smart programming and predict client investment strategies. Also these data can be used to predict fraudulent activities.
The application of Machine learning in wealth management
Step-1
Machine learning can play key role in optimized portfolio selection. In quantitative portfolio management AI can investigate driving factors such as analysis across diversified portfolio, historical data, equity value , risk management and other key driving factors. Mathematical models can be developed from such data management.
Step-2
Risk management is an important factor for portfolio management and hence AI can help identify diversified stock and allocate the proportionate weight to minimize risk.
Step -3
Identify the timelines and level of risk for individual investment portfolio. Perform forecasting analysis from available data
Step-4
Change the variables to identify the ideal investment portfolio that gives the best ROI ( Return on investment).
Schematic prototype for portfolio management of a client using AI
The quantitative algorithm for the design of a smart AI driven portfolio management is relatively complex. The aspect of cybersecurity also becomes a vital parameter that needs to be incorporated into the design. However an approach is made to show a brief overview of the proposed user interface and the schematic flowchart design for client’s wealth management.
The mobile or desktop user interface (UI) is a primary goal that needs to be carefully designed to ensure customer engagement with the application or platform. The user login and password encryption are part of ensuring customer privacy and security of data.
In order to obtain the optimum portfolio for your client that caters to their expectation, it is vital that their expectation is well understood. This usually involves a series of questionnaire that can target towards knowing your client better. The questions could include specifics e.g: whether your client aims for a long term investment( minimum of one year) or rather a short term investment (3-months). The maximum amount your client is willing to invest, the risk tolerance, e.g if a client has low risk tolerance safer options like government bonds (is preferred) or for higher risk tolerance investors can opt for Wall Street investment(high risk-high reward). The risk-return concept needs to be understood and the algorithm should be coded to best tailor risk-return expectation.
The c++ code below can give a brief overview of the expected inquiry between the client and the fintech platform about their portfolio management.
Monte Carlo Tree Search (MCTS) is the AI driven probabilistic algorithm. It involves analyzing the different options to find a more optimal one than the current action. MCTS algorithm evaluates the alternate strategies during its learning phase ( machine learning) and determines whether it offers a better return than its former or present transaction. This technique is already applied in simulated gaming environments but can offer better forecasting outcomes when applied to financial sectors.
Conclusion
To answer the first question on the topic, it is an absolute YES. The integration of AI in the financial sector can consistently show improved performance. Machine learning offers a faster evaluation and execution of vast financial data. It is relatively less time consuming and can process a majority of data with higher accuracy than human interactions. The fintech startups showing a faster growth rate is an indicator of the rising demand of this technology in the market.
References
[1]Schüffel, Patrick (2016). Taming the Beast: A Scientific Definition of Fintech. Journal of Innovation Management. p. 32–54
[2]Leong, K., & Sung, A. (2018). FinTech (Financial Technology): what is it and how to use technologies to create business value in fintech way?. International Journal of Innovation, Management and Technology, 9(2), 74–78.
[3]Calcaterra, C., Kaal, W. A., & Rao, V. K. (2019). Stable cryptocurrencies-first order principles. Available at SSRN 3402701.
[4]Ta, V. D., Liu, C. M., & Tadesse, D. A. (2020). Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network in Quantitative Trading. Applied Sciences, 10(2), 437.
[5]Fabozzi, F.J.; Markowitz, H.M. The Theory and Practice of Investment Management: Asset Allocation, Valuation,Portfolio Construction, and Strategies, 2nd ed.; John Wiley and Sons: Hoboken, NJ, USA, 2011; Volume 198,pp. 289–290.
[5]Lucia, R. J. (2012). U.S. Patent Application №12/957,211.
