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Applications of Data Science in Banking and Financial sector.pptx

  2. Table of contents • About the Domain • Why the Banking domain is Essential in Finance • Services Provided by the Banking Institutions • Major Risks Faced by the Banking Institutions • Applications of Data Science in Solving the Problems of the Domain • Real Life Example: JP morgan's Use of Data Analytics • Challenges and Use cases of the Banking Domain Testing • Conclusion
  3. The Domain - Banking and Finance The financial sector is the segment of the economy comprising businesses and institutions that offer financial services to both consumer and corporate clients. An economy is in good shape if its financial sector is vital. The sector comprises many sectors, including banking, investing, insurance, and real estate organizations. Some of the most well-known banking institutions in the world are among the biggest firms in the financial industry, including the following: • JPMorgan Chase (JPM) • Wells Fargo (WFC) • Bank of America (BAC) • Citigroup (C)
  4. Why the Banking domain is important in Finance • The Banking domain comprises all the components that are required to run financial service end to end. It encompasses the transaction and distribution process; the manner in which consumers engage with the system, the goods, and services the business delivers; and the technology involved. • The area of technology and processes is the area of banking that is the widest. The technology utilized to meet performance goals, the manner the organization manages its personnel, their roles and duties, and the procedures that clients must adhere to in order to complete a transaction are all included. • Company managers can set the checkpoints required to enhance the institution's performance by using a banking system as a framework. The elements of the banking domain like the customers, the particular niche of banking institutions, products and services, distribution and sales, and technology, are widely relied upon by both developers and testers of financial applications.
  5. Services Provided by Banking Institutions Banking institutions involve advancing investment banking solutions for various businesses, organizations, and governments, such as mergers and acquisitions, capital raising, and risk management. They also offer insights into investment banking and the larger fields of finance, economics, and markets. Healthcare, technology, mergers and acquisitions (M&A), shareholder involvement, and other industries are covered. A firm's interactions with the India-based subsidiaries, branches, liaison offices, or project offices of its American clients are managed by Commercial Bank International in India (CB- India). As customers increase their presence in India, the organizations part of this domain, offer them local knowledge, consultative assistance, and complete banking solutions.
  6. Major Risks Faced by the Banking Sector are: Credit risk The most prominent risk facing banks is credit risk. When counterparties or borrowers breach contractual duties, it happens. One instance is when borrowers fail to make a loan payment for the principal or interest. Mortgages, credit cards, and fixed-income assets are all subject to default. Derivatives and offered guarantees are other instances where obligations may not be met. Operational risk is the possibility of suffering losses due to inaccuracies, errors, or damage brought on by people, systems, or procedures. Operational risk is lower for straightforward business activities like retail banking and asset management and greater for activities like sales and trading. Internal fraud and transaction errors are examples of losses brought on by human error. On a larger scale, fraud can occur by breaching a bank’s cybersecurity.
  7. Market risk mainly results from a bank's capital market activity. Credit spreads, interest rates, commodity prices, and equity markets are unpredictable. Liquidity risk The capacity of a bank to get money to satisfy financing obligations are referred to as liquidity risk. If a bank delay giving some of its clients cash for a day, other depositors can rush to withdraw their money as they lose faith in the bank. Overreliance on short-term funding sources, a balance sheet heavily weighted in illiquid assets, and a decline in client confidence in the bank are some causes of banks' liquidity issues.
  8. Banking analytics refers to the use of artificial intelligence and machine learning to analyse customer data and make choices in the banking industry. Using such analytics, data is examined, patterns are found, and forecasts are created. The banking domain needs to be aware of the must-have characteristics a successful software tool has to offer like: 1. Secure user authentication mechanisms 2. Built-in management system 3. QR payment support 4. ATM locator 5. Real time processing and batch processing
  9. Risk Analysis The banking sector places a strong focus on risk modeling. It aids them in developing fresh methods of performance evaluation. One of its most crucial components is credit risk modeling. Banks can use credit risk modeling to examine how loans will be repaid. There is a possibility that the borrower won't be able to pay back the loan in credit hazards. Credit risk is complicated for banks to manage due to its many variables. Banks can use risk modeling to analyze the default rate and create plans to strengthen their lending programs. Before authorizing a loan in a high-risk situation, banking companies can analyze and categorize defaulters with the use of big data and data science. Applications of Data Science in solving Problems in the Banking & Financial Sector
  10. Fraud Detection Machine learning breakthroughs have made it simpler for businesses to identify fraud and anomalies in transactional patterns. Monitoring and analyzing user behavior for predictable or harmful patterns is part of fraud detection. Utilizing data science, businesses may develop clustering tools that will aid in identifying various trends and patterns in the ecosystem for fraud detection. These tools will make use of machine learning and predictive analytics. Different methods, such as K-means clustering and SVM, are useful in constructing the framework for identifying odd activity and transaction patterns. Customer Analytics Banks can use predictive analytics to categorize potential clients and assign them with high future value so that the firm can focus on them. While the categorization algorithms assist the banks in attracting new clients, keeping them is a difficult challenge. Various tools are employed in the preprocessing, cleaning, and predicting data. They include Generalized Linear Models (GLM), Classification and Regression Trees (CART), etc.
  11. Customer Segmentation Banks divide their customer base to serve client needs depending on their behaviors and shared traits. In this case, segmenting clients based on similar behaviors and identifying future customers rely heavily on machine learning techniques like classification and clustering. K-means is a well-liked clustering method that is frequently employed for grouping related data points. Customer segmentation can be helpful to financial institutions in the ways of identifying customers depending on how profitable they are, dividing clients into groups according to how they use banking services, and strengthening their connections with their clients. Recommendation Engines One of the key functions of a bank is to offer clients individualized experiences. Offers and additional services are suggested using consumer transactions and personal information data. After reviewing past transactions, banks also make an educated guess as to what items the consumer could be interested in purchasing.
  12. Predictive and real-time Analytics The practise of utilising computer methods to forecast future occurrences is known as predictive analytics. Predictive analytics' primary toolkit is machine learning. The best instrument for enhancing the banks' analytical approach is machine learning. Data analysis is more important than ever because of the exponential growth of data, which has led to a plethora of use cases.
  13. JP MORGAN'S USE OF DATA ANALYTICS JP Morgan uses Hadoop to analyze data and detect fraudulent activities, to add value to the consumers. it uses predictive analysis to forecast its clients' effective cash management practices. JP Morgan's clients may get clear information using the "CreditMap" application. Real-time analytics are offered to the clients via the Datawatch platform. the organization uses big data analytics to use public information and help policymakers prevent financial disasters. JPMorgan explores, combines, and securely analyses various cyber datasets using Sqrrl's big data analytics platform. JP Morgan uses big data to read the US economy, for fraud detection, to get a clear perspective of credit market data, for effective cash management, and to enrich customer experience.
  14. Challenges and Use cases in Banking Domain Testing Testing the applications in the banking domain is challenging. Assessing these tools requires a high level of financial expertise and knowledge in data analysis. Following are some challenges that are popularly faced by analysts while testing applications in this domain: • Implementing a strict security system • Complex database • Integrations with other tools • Real-time data support • Active device support
  15. Conclusion Testing banking domain applications is essential since it gives business owners perspectives they might not have known before. It is preferable to take time and identify every problem while the project is still in development rather than correcting problems in a hurried atmosphere after the app has been released. Banks must recognize the critical role of data science, incorporate it into their decision-making process, and create strategies based on useful insights from their client's data to acquire a competitive edge. The approaches and tools provided by data science may increase the precision of risk management, and the caliber of customer service, as well as automate and speed up various business operations, so improving the organization's overall efficiency. Adopting innovative techniques and algorithms for handling timely information is crucial for remaining competitive and boosting profitability.