Applications of Data Science and Machine Learning in Banking

“Slow and Steady Does Not Win the Race”

In this last year of the decade, there are drastic changes in the Banking sector in terms of customer experience, technology and innovation. In this tough time Bankers went through many changes and challenges. Todays banking is nothing like what it used to be 10 years ago and it will not be same after 5–6 years down the line.

Earlier banks use to build their trust through personal relationship. But now, in this cutting edge competition, providing best, relevant and affordable banking service is the key to capitalize the market and build the relationship.

Data is at the very heart of today’s digital transformation. Banks are sitting on a goldmine of data that resides in their connection with corporate ERP systems. The only question that remains is, does banks have right resources, tools to harvest and use this data to help clients better manage the relationship with their customers.

With the advancement of the technology, now the banks have better ability to know their customers, anticipate their needs and offer relevant products and services.

“What’s changed with recent advances in technology and data science is that now we are able to use that data much more effectively, to understand our customers better, to serve them better and help them achieve their financial aspirations and goals,” says Shameek Kundu, Chief Data Officer for Standard Chartered Bank.

Following are some changes Data Science can bring into the banking operations.

Fraud Detection

According to the IC3 (Internet Crime Complaint Center), financial losses caused by fraud in 2019 were at its highest ever; the IC3 processed almost 500,000 complaints. In addition, the IC3 reported that business and personal losses in 2019 were almost $3.5 billion higher than in 2018.

Data analytics tool like Machine learning based solutions can process massive amount of data at a speed beyond human capabilities. With these tools you can spot the anomalies in the data quickly and efficiently.

Some factors on my ML techniques are so popular and widely used in industries for detecting frauds.

• Speed: Machine Learning is widely used because of its fast computation. It analyzes and processes data and extracts new patterns from it within no time. For human beings to evaluate the data, it will take a lot of time and evaluation time will increase with the amount of data.
• Scalability: As more and more data is fed into the Machine Learning-based model, the model becomes more accurate and effective in prediction.
• Efficiency: Machine Learning algorithms perform the redundant task of data analysis and try to find hidden patterns repetitively. Their efficiency is better in giving results in comparison with manual efforts. It avoids the occurrence of false positives which counts for its efficiency.

Managing customer data and Personalized Banking

Managing customer data is not just a compliance exercise. But rather Data Science and Machine Learning can transform this exercise into a possibility to learn more about their clients behavior to drive towards new revenue opportunities.

Data scientists utilize the behavioral, demographic, and historical purchase data to build a model that predicts the probability of a customer’s response to a promotion or an offer

With digital creates terabytes of customer data, thus the first step of data scientist is to isolate the relevant data. Once this is done, you slice and dice the customer data to understand the customer behavior & preferences and with the right ML models you can unlock new revenue opportunities. Banks can customize the product, offers and services for the targeted customer.

Risk Management

Every firm needs to prepare for disasters. But the need for such is highest in the finance industry. Banks may also manage and reduce the risk by assessing customer profiles. Thus the power of Data science and ML tools can be leveraged for effective risk modeling and for better data driven decisions.

Recommendation engines

Data science and ML tools can be used to create recommendation engines which can analyze the customers activity and suggest him the most relevant and accurate product or service. This may interest the user even before he searched for himself.

To build a recommendation engine, data specialists analyze and process a lot of information, identify customer profiles, and capture data showing their interactions to avoid repeating offers.

Conclusion

To gain competitive advantage, banks must acknowledge the crucial importance of data science, integrate it in their decision-making process, and develop strategies based on the actionable insights from their client’s data.

Thanks to rapidly developing data science spectrum, banks can expand their horizon by applying machine learning models to real data and gain more accurate results.