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SeekACE approach towards application of
Data Science in Banking Sector.

Published Date -

February 12, 2020



If you are trying to learn Data Science or in your life or you are passing through lot of jargons, at SeekACE we make things sweet and simple. In today’s discussion, lets walk through our approach to develop an application for banking sector. Like all other industrial verticals banks deals with lot of customers every day and there by produces a lot of data. The basis of banking business is to lend money and earn interest. This involves different types of risks and sometimes dealing with fraudulent people. Now let us find out how Data Science can help banks to mitigate different risks and help to identify a fraudulent loan applicant.

Let’s start with mitigating risk for banks. Here we will be introducing some modeling techniques.

  1. Risk Modeling

    Risk Modeling is a high priority for the banking industry. It helps them to formulate new strategies for assessing their performance. Credit Risk Modeling is one of its most important aspects. Credit Risk Modeling allows banks to analyze how their loan will be repaid.

    In credit risks, there is a chance of the borrower not being able to repay the loan. There are many factors in credit risk that makes it a complex task for the banks.

    With Risk Modeling, banks are able to analyze the default rate and develop strategies to reinforce their lending schemes. With the help of Data Science, banking industries are able to analyze and classify defaulters before sanctioning loan in a high-risk scenario. Risk Modeling also applies to the overall functioning of the bank where analytical tools used to quantify the performance of the banks and also keep a track of their performance.

    At SeekACE, under the leadership of academic group, a development team is working closely under Mr. Abhay Singh, who is the Project Manager. At SeekACE, under the leadership of academic group, Mr. Abhay Singh, the Project Manager leads a development team. Here we mostly analyze different Statistical models to find out how to quantify the performance of any bank. The problem starts with descriptive method to understand under what conditions loans will be repaid or not repaid. The second issue is to predict under what criteria loans may be disbursed. Third and the most important part is to prescribe a basket of loans which will minimize the risk for a given bank for group of customers.

  2. Fraud Detection

    With the advancements in machine learning, it has become easier for companies to detect frauds and irregularities in transactional patterns. Fraud detection involves monitoring and analysis of the user activity to find any usual or malicious pattern. With the increase in dependency on the internet and e-commerce for transactions, the number of frauds has increased significantly.

    Using data science, industries can leverage the power of machine learning and predictive analytics to create clustering tools that will help to recognize various trends and patterns in the fraud-detection ecosystem. There are various algorithms which we use at SeekACE like K-means clustering, SVM that is helpful in building the platform for recognizing patterns of unusual activities and transactions. The process of Fraud Detection involves –

    Obtaining the data samples for training the model.

    Training our model on the given datasets. The process of training involves the implementation of several machine learning algorithms for feature selection and further classification.

    We also learn to Test and Deploy our model at SeekACE.

    For instance, two algorithms like K-means clustering and SVM can be used for data-preprocessing and classification. K-means can be used for feature selection and SVMs are then applied to the data for its classification into a fraudulent class or otherwise.

    Our approach is to create a solution for different banks and in the process, develop a resource pool of talented persons who can also leverage their potential to grow as Data Scientist. This holistic approach in turn narrows the gap of requirement for Data Scientists and the availability of trained man power. There are lot of challenges in this process. One of the biggest challenges is getting appropriate data. Most of the financial data is masked. We understand that confidentiality is a must and we maintain secrecy of data. We follow ISO 20000 standard for the same.

    Thank you for reading this article. Let me know through a feedback. In the following articles, I will be discussing more about other applications of Data Science in banking sector.


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