data science in finance

We have all heard these buzz words- Fintech and Digital transformation. Well, you must think, what’s fintech and how does it affect how I bank?

Fintech refers to software, algorithms, and applications for both computer- and mobile-based tools that are used to augment, streamline, digitize or disrupt traditional financial services.

Well, banks are an integral part of our economic system and we can’t imagine a world without them. I’ll be focusing on how digital transformations especially with the inclusion of data science and data-driven decisions can bring new customer experiences to the banking sector.

In the journey of digital transformation, banks across the world have been catching up quite well in the adoption of advanced analytics, data-driven decisions, process automation, etc. in their everyday processes.

The inclusion of such technologies gives banks a competitive advantage, as they integrate it in their decision-making process, and develop strategies based on the actionable insights from their customer’s data. Data science and AI not only strengthen the efficiency, cost-effectiveness, customer experience, regulation, and security of existing economic-financial systems, it also helps build more intelligent, secure, and proactive products and services that form the new area of smart Fintech.

There are a plethora of applications where data science can be a real game-changer for the business. Customer relationships, product marketing, risk management, cost control, fraud detection, etc., all revolve around data, its quality, structure, and its integration in the internal systems.

While many banks have already jumped on the bandwagon and adopted data-driven decisions, data warehouses, and automation through Robotic process Automation, there are a lot of use-cases that have been a great feature addition for not only improving the customer experience but also improving the business revenue for the firms. Let’s look at some of these with more detail:

1. Fraud Detection

With an ever-increasing number of transactions happening both online and through the traditional methods, there’s a lot of room for bank fraud. Every year, fraud costs the global economy over $5 trillion. In addition to taking a toll on individual victims, fraud impacts businesses in the form of lost revenue and productivity as well as a damaged reputation. A fraud prevention system is set up to look for unusual transactions based on customers’ transaction patterns and alert the end-users in such cases. It could be for old or new customers. For instance, for a newly onboarded customer, it may detect multiple accounts opened in a short period with similar data. It’s a knowledge-intensive activity. Fraud related to card transactions can also be identified by looking at the historical transaction data. In the current ecosystem, a lot of AI and ML techniques including data mining, clustering, classification, and segmentation is used to automatically find associations and rules in the data to identify complex patterns, including those related to fraud.

So the next time you get a call from your bank when you’re on a shopping spree on a Friday, you know what systems alerted the bank of potential fraud.

2. Customer Acquisition and Retention and personalization

Banks and Financial firms can leverage internal and external customer data to create comprehensive customer profiles which can be used to tailor customer experience and provide highly personalized offers for card solutions, loan and credit card offers. For example, an algorithm could be built to predict what additional products or services the customer would like to purchase based on their historical purchase behavior. AIML techniques like Collaborative Filtering, Sequence aware recommenders like RNN and LSTMs, and even reinforcement learning models.

3. Trading and Cryptocurrency

Being able to predict how the markets are going to move in the next 10 mins or even 10 seconds is an idea of Data Science and AI in Fintech. Leveraging the mathematical algorithms, we can are able to use real-time data from both unstructured and structured sources to find underlying patterns and trends that might otherwise have been hidden. By spotting trends and risks, these algorithms allow customers, companies, banks, and additional organizations to have a more informed understanding of investment and purchasing risks. While high-frequency, algorithmically-determined trading has always been around, traders were often limited by what they could do in Excel. It’s very difficult to understand the volatile markets data science techniques alleviate this by allowing traders to combine data from a plethora of sources and examining massive amounts of data on past market activity. Aside from this, the benefit of data science in trading practices is reproducibility. Quickly and reliably, information can be distilled that would be impossible for even the most diligent of traders to find before.

4. Credit Risk Analysis

Credit risk is the possibility of a client failing to meet contractual obligations, such as mortgages, credit card debts, and other types of loans. Traditionally, various methods such as credit score cards, intelligent dashboards reporting templates, etc. were used in determining the various risk parameters. Over the years, there has been a shift towards a more data-driven approach to assessing credit risks.

Credit scoring companies like Experian, FICO, and many others leverage data science and machine learning techniques to provide instant financial data on borrowers. For example, companies can use boosting algorithms like XGBoost and decision trees to predict the probability of default of potential loan borrowers taking into account the person, their credit history, delinquency rate, and a multitude of other factors.

The use of AI and L algorithms is not only limited to predicting default probability but can also be extended to estimate recovery rate (The rate at which loss could be recovered by selling customer collateral, given the customer defaults, Loss Given Default (The fractional loss due to default) and Exposure at Default (The amount owed at the time of default)

While these are some examples of how data science can be used in Fintech, there are limitless possibilities for its application. There’s a lot of potential for digitizing each and every process in the Financial technology space. The impact of the digital transformation has been multifold in the post covid times, from paying through UPI’s, getting instant loans approved online, getting fast insurance, personalized offers, and an improved risk analysis leading to the reduced number of frauds. Fintech has radically altered the financial landscape by facilitating the application of big data, AI & ML algorithms, and complex calculations to financial decision-making. In my opinion data science and fintech co-exist. Data science is indeed the backbone of how fintech firms make decisions about everything from customer personalization and acquisition to online working capital loans. Data science and Big data have truly revolutionized the business world!

I am currently working with Goldman Sachs and building all things data @Marcus by Goldman Sachs, India. I am a big-time Machine Learning aficionado. In the past few years, I have worked with Computer Vision and Music Genre Analysis. While I am not working, I build AI and ML-based applications for social good and work on building applications at scale while at work. I love participating in hackathons. I am a serial hackathon winner (Microsoft AI hackathon, Sabre Hack, Amex AI hackathon, Icertis Blockchain, and AIML hackathon, Mercedes Benz) and people often call me "The Hackathon Girl". I am a tech speaker, tech blogger, podcast host, hackathon mentor @MLH hacks, technical content creator at Omdena, and Global Ambassador at Women. Tech Network.I believe in hacking my way through life one bit at a time.
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