Deploying AI to modernise Financial Services: An intro to FinTech

by Arun P Teja, Parvathi Bakshi and Dalalstreet.ai

Introduction

Artificial Intelligence (“AI”) has become a buzz word among the business and political elite across the world. It has gone to such an extent that Russian president, Putin asserts that the – the state that controls AI would rule the world (Vincent, 2017). Further, think tanks which advice the government opine that it is a once in a decade technological innovation (Niti Aayog, 2018). Yet there is hesitation among political stakeholders to adopt these new technologies, as successful large scale uses cases of AI are few in number. Unless AI innovators are able to prove immense economic impact, the chances of AI being deployed in national or foreign policy is likely to be met with scepticism. However it cannot be denied that AI, with being a fundamental functionality of the fourth industrial revolution, is being considered to be the future of industries across the horizontal spectrum. One such area where AI has been hugely successful in terms of acceptance and successful uses cases has been in the financial sector (Gokhale, Gajjaria, Kaye, & Kuder, 2019). In this short article, we will discuss what is ‘FinTech’ and the new age technology being deployed therein. Next, we look at how AI, especially through its integration in financial technology has made leaps towards revolutionising financial services, through two examples – e-payments and robo-advisory. Finally, we will end with a case study on a Fintech start-up in India and how it is using AI driven algorithm to render its services in the stock-market.

Financial Technology – FinTech

The term FinTech is actually quite clear cut to mean technology in finance, but we’re not talking about the dated computers which made bookkeeping and stock trading online, rather we’re talking about the cutting-edge, sci-fi tech which we see in movies – Artificial Intelligence. Before we begin explaining how AI has transformed the manner in which financial services are rendered today, we must understand the kind of AI  we are looking at. The uniqueness of AI is that it transcends boundaries and can be utilised in any industry according the nature and problems to be solved within that industry. Therefore, it is natural that the tasks, problem-solving capacity and programming of AI will also differ according to the industry. It is important to understand that a one-size-fits-all AI doesn’t per se exist but the general idea that computers which replicate human intelligence does solve problems in many industries does exist. Within the financial industry, various players involved in financial management, asset management, payments, advisory services and more have aggressively begun to adopt AI and its subset Machine Learning (“ML”), to render better financial services. Fintech is thus inclusive of technology such as AI, blockchain, Big Data, Machine & Deep learning and e-payments. The main goals of fintech include utilising tech such as AI and ML,  to reduce transaction costs; reach larger audiences; focus on customer experience; and take advantage of economies of scale. One key step to doing this has been to dis-intermediate humans and replace their role with AI systems which are able to process data far more efficiently and thus render quicker, more useful results than a team of humans. While dis-intermediation may be resisted by various market players, it is undeniable that replacing certain traditionally human roles with AI has improved efficiency and quality of services.

How does technology augment financial services?

  • E-Payments

Some of the key places where technology has disrupted financial services has been in mobile payments and banking services. Where earlier transferring money domestically or internationally was a long and tedious process, now with apps like Paypal, Venmo, Google Pay and Paytm to name a few, financial inclusion has become an achievable goal. In India, the Unified Payments Interface (aka UPI) which is a real-time payments system facilitates inter-bank transactions mapped using bank account number, Aadhar linked account or virtual payments address (National Payments Corporation of India, 2020).  With its simple 2 factor authentication and various other application programming interfaces, has revolutionised payments in India. With UPI, e-commerce and fintech reach into the rural depths has been possible, allowing innovators to build solutions for financial services to health care services.

  • Artificial Intelligence & Machine Learning

A cutting-edge example of financial services incorporating technology is the Robo-advisory space. Robo-advisors are employed by financial advisory firms and are essentially ML algorithm-based computing that provide services based on inputs or data collated online. Robo-advising has been extremely popular in capturing customer financial information, performing risk assessments, recommending portfolios based on output and also automated reporting. Some simpler applications of robo-advising includes AI chatbots that firms use for customer relationship management to answer queries about investments and other standard queries. A disclaimer is necessary to state that most robo-advisory firms are not fully automated but follow a bionic model or a model where the approach to financial advice incorporates both technology and human interactions. 

Robo-advisory specialises in building personalised investment strategies with detailed questionnaires to investors and also automated capture of clients information (Deloitte, 2016). AI analysing the available Big Data, is a huge asset in these spaces where knowing your client and what they need can be understood from looking at their behaviour. One way of doing this is by programming algorithms which look at short/long-term financial goals, risk tolerance, risk aversion, asset classes so on and then let the algorithms identify patterns with the mass of data. These patterns that a ML algorithm may identify, can show trends preferred financial goals, likelihood of risk aversion and more. Financial advisory firms tend to prefer a ‘the more we know, the better’ approach towards investing for a client. With AI and ML, robo-advisory has transformed a large class of investors with low level assets to a more economically viable class of investors.

Conclusion

From the discussion, we have seen how financial services are being digitalised entirely. All the vertical layers of financial services are being augmented with technology. In this essay, it is clear from our discussion of e-payments and robo-advisories, how (a) newer services perform functions humans couldn’t and (b) humans are being dis-intermediated and their traditional roles are being conferred to machines. People are no longer bound by paper currency and now rely on a digital contract of bearing the total sum of rupees. With the efficient encryption layers and protocols, such contracts are being ever secured. Transcending this application of digital technology in financial services, AI has leaped in the area of human –technology augmentation. Robo-Advisors programmed with the black box of neural networks are seen to be capable enough to advice on the investments and help people to make efficient decisions. However, the fears that they would replace humans in the area of wealth management are bit dystopian. Today’s AI which is called Artificial Narrow Intelligence or Weak AI is only capable of pattern recognition and data analysis. Such application of AI would help in increasing human capacity and provide quick and efficient financial services to public. To have more understanding of the latter, let’s have a quick peek into our case study of Fintech start-up which uses AI to predict the stock price in India. 

We’ve chosen a case study as a way to understand how innovators in the finance industry are adopting new age technology. As we mentioned earlier, there is no one-size-fits-all when it comes to AI and most start-ups as well as even existing financial companies are utilising AI in a new and unique manner. The following case study is an instance of AI application in the stock market. First the AI helps new beginner investors and traders to select economically viable investments that are suited to their investment goals. Then the AI helps manage those investments by predicting when to make buys/sells or other actions on those investments (aka trades).

Case Study: Dalalstreet.ai – Algorithm based trading manager

Dalalstreet.ai, is an innovator in the FinTech space is working on boosting the equity market by developing a highly advanced AI algorithm. By relying on weak AI or say Artificial Narrow Intelligence they utilise this technology to help beginner traders in their investment decision making.

The idea is to provide equity market predictions for when to buy/sell or hold an equity, and it helps traders to minimise the risk of making false decisions without putting much effort in studying the equity market. Normally, beginner traders would not have an understanding of what it takes to make a trading decision because it requires sentimental, technical, and fundamental analysis. Hence, non-specialised individuals who attempt to invest their money in equities while they do not have basic knowledge of the equity market tend to lose out on their investments

Dalalstreet.ai has designed an algorithm to help traders and non-traders who want to trade in equities with the minimum effort and maximum profitability. Traders can potentially rely on the AI to make decisions rather than conducting analysis and trying to make decisions based on their own analysis which wouldn’t pass a basic cost-benefit analysis test.

The algorithm is designed and developed with a neural network which is an AI technique that can mimic the certain operations of a human brain. Further, it is trained to gather company data, evaluate technical indicators, and read the latest equity market news which can result in providing advice for early traders. AI has the ability to perform these tasks in a shorter period than humans, so if it is implemented in the capital market of the FinTech industry; it can enable traders to focus on their daily tasks while depending on our system predictions to manage their portfolio. An additional benefit is providing a user-friendly platform interface so that the trader gets familiar with the technology and enjoys the trading.

AI algorithms like that at Dalalstreet.ai are creating disruptions in the investment market. Not just the fundamental and technical dimensions but new alternative data which impacts the stock market has become a new variable in the stock price prediction. With the help of deep learning algorithms, incorporating more and more variables to increase prediction outcomes and aid investors in making the highly efficient decisions is an upcoming reality.

Bibliography

1. Vincent, J. (2017, September 4). Putin says the nation that leads in AI ‘will be the ruler of the world’. Retrieved June 3, 2020, from The Verge: https://www.theverge.com/2017/9/4/16251226/russia-ai-putin-rule-the-world?utm_campaign=theverge&utm_content=chorus&utm_medium=social&utm_source=twitter

2. Niti Aayog. (2018, June). National Strategy for Artificial Intelligence. Retrieved April 12, 2020, from Nitiayog: https://niti.gov.in/writereaddata/files/document_publication/NationalStrategy-for-AI-Discussion-Paper.pdf

3. Harari, N. Y. (2017). Homo Deus: A brief History of Tomorrow. London: Vintage.

4. National Payments Corporation of India. (2020). Unified Payments Interface. Retrieved from NCPI: https://www.npci.org.in/product-overview/upi-product-overview

5. Deloitte. (2016, August). The expansion of Robo-Advisory in Wealth Management. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/de/Documents/financial-services/Deloitte-Robo-safe.pdf

6. Gokhale, N., Gajjaria, A., Kaye, R., & Kuder, D. (2019, August 13). AI Leaders in Financial Services. Retrieved from Deloitte: https://www2.deloitte.com/us/en/insights/industry/financial-services/artificial-intelligence-ai-financial-services-frontrunners.html


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