A STUDY ON BANKING INNOVATIONS THROUGH DATA SCIENCE
- 1. Banking Innovation ISBN 978-93-93996-89-3 85 7 A STUDY ON BANKING INNOVATIONS THROUGH DATA SCIENCE K. GAYATHRI Part-time Research Scholar, Department of Commerce, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, Tamil Nadu, India – 600 117. Dr. K. MAJINI JES BELLA Assistant Professor & Research Supervisor, Department of Commerce, Vels Institute of Science Technology and Advanced Studies, Pallavaram, Chennai – 600 117. Abstract The aim of this article is analyse how the data science has played a significant role in driving banking innovations in recent years. By leveraging big data analytics, machine learning, and artificial intelligence, banks are transforming their business operations and customer experience. One major area where data science is impacting banking is fraud detection. Banks are using sophisticated algorithms to analyse customer data, identify unusual patterns, and detect fraudulent transactions in real-time. This helps banks reduce losses due to fraud and provides a better customer experience by preventing unauthorized transactions. Data science is also being used to personalize banking services. By analysing customer data, banks can provide tailored recommendations for financial products, such as loans and credit cards that match the customer's needs and preferences. Another area where data science is being applied in banking is risk management. Banks are using predictive analytics
- 2. Banking Innovation ISBN 978-93-93996-89-3 86 to assess the creditworthiness of borrowers, detect potential defaults, and identify market trends that could impact their business. This helps banks make informed decisions and manage risk effectively. The data science is driving banking innovations by enabling banks to make more informed decisions, improve customer experience, and increase efficiency. KEY WORDS: Banking, Innovations, Data Science, Market trends, Customer, Efficiency, Customer experience, Satisfaction, Operations and Risk. 1. Introduction As the amount of data generated by banking operations continues to grow, data science will become even more critical to the success of banks. The banking sector is an important component of the financial system and plays a crucial role in the economy. The banking sector consists of various types of institutions such as commercial banks, investment banks, and credit unions. These institutions provide a range of financial services to individuals and businesses, including loans, deposits, and investment products. The banking sector is heavily regulated to ensure the safety and soundness of the financial system. Regulators such as the Federal Reserve, the FDIC, and the OCC oversee banks and enforce regulations that aim to prevent bank failures and protect customers. Paramasivan C & Ravichandiran G (2022), Technology is one of the major parts of banking sector which decide the quality and effectiveness of banking services. Inclusive banking services to un banked people will be possible only with the help of innovative business practices. With this view, this study will provide an output to understand the impact of innovative business practices of banking with respect to socio-economic development. a. Technology The technology has a significant impact on the banking sector, with innovations such as mobile banking, contactless payments and AI-powered chatbots transforming the way banks interact with customers and deliver services.
- 3. Banking Innovation ISBN 978-93-93996-89-3 87 b. Globalization The banking sector is increasingly global, with many banks operating across multiple countries and continents. This globalization has led to increased competition, but also increased regulatory complexity and challenges. c. Financial intermediation One of the key functions of the banking sector is financial intermediation, which involves channelling funds from savers to borrowers. Banks play an important role in allocating capital and facilitating economic growth by providing loans and other financial services. The banking sector is an essential component of the financial system and the economy, playing a crucial role in facilitating economic growth, managing risk, and providing financial services to individuals and businesses There have been a lot of banking innovations in recent years that have changed the way of banks and manage the finances. d. Mobile banking With the widespread adoption of smartphones, mobile banking has become one of the most popular banking innovations. Customers can now access their accounts, transfer funds, pay bills, and even deposit checks using their mobile devices. e. Contactless payments With the rise of contactless payments, customers can now pay for goods and services using their phones or contactless cards, making transactions faster and more convenient. f. Digital wallets Digital wallets such as Apple Pay, Google Wallet, and Samsung Pay allow customers to store their credit and debit cards on their phones and use them for contactless payments. g. Robo-advisors Robo-advisors use algorithms to provide investment advice and portfolio management services, make investing more accessible and affordable for the average person.
- 4. Banking Innovation ISBN 978-93-93996-89-3 88 h. Open banking Open banking allows third-party financial service providers to access a customer's financial data, with their consent, to offer personalized financial products and services. i. Crypto currency Crypto currency, such as Bitcoin and Ethereum, has disrupted traditional banking and financial systems by providing a decentralized and secure way to conduct transactions. j. AI-powered chatbots AI-powered chatbots can answer customer queries, provide support, and even help customers open new accounts or apply for loans. 1.1 DATA SCIENCE Data science has enabled banks to leverage vast amounts of data to make informed decisions and provide better services to customers. Here are some of the banking innovations that have been made possible through data science. a. Fraud detection: Data science techniques such as machine learning and anomaly detection are used to detect fraudulent transactions and prevent them from occurring. Banks can analyse historical data to identify patterns and anomalies that may indicate fraudulent activity, and use this information to develop algorithms that can automatically detect and flag suspicious transactions. With the rise of digital transactions, fraud has become a major concern for banks. Data science algorithms can analyse large amounts of transaction data to detect patterns and anomalies that may indicate fraudulent activity. b. Personalized marketing: Banks can use customer data to create targeted marketing campaigns that are personalized to the customer's interests and preferences. For example, a bank may analyse a customer's spending habits to offer them a credit card with rewards that are relevant to their spending patterns.
- 5. Banking Innovation ISBN 978-93-93996-89-3 89 c. Risk management: Data science can help banks assess and manage risk by analysing data on market trends, customer behavior, and other factors that may impact the bank's operations. Banks can use this information to develop risk models and make informed decisions about lending, investment, and other activities. Banks use data science to assess the creditworthiness of borrowers, detect potential defaults, and identify market trends that could impact their business. This helps banks make informed decisions and manage risk effectively. d. Customer service: Banks can use data science to improve customer service by analysing customer feedback and behavior to identify the areas for its improvement. For example, a bank may analyse customer complaints to identify common issues and develop solutions to address them. e. Credit scoring: Data science has enabled the banks to develop more accurate credit scoring models, which can help them assess a borrower's creditworthiness and make more informed lending decisions. By analysing large amounts of data, banks can identify the factors that are predictive of credit risk and use this information to develop models that are more accurate and reliable. These are just a few examples of how data science has enabled the banks to innovate and improve their services. As banks continue to collect and analyse more data, we can expect to see even more innovative uses of data science in the banking industry. 1.1.1 IMPORTANCE OF DATA SCIENCE IN BANKING SECTOR The importance of data science in banking cannot be overstated. Data science has become a critical tool for banks to manage risk, detect fraud, and provide personalized services to customers.
- 6. Banking Innovation ISBN 978-93-93996-89-3 90 1. Personalization: By analysing customer data, banks can provide tailored recommendations for financial products that match the customer's needs and preferences. This helps to increase the customer satisfaction and retention. 2. Efficiency: Data science can help the banks in automate repetitive tasks, such as data entry and report generation, which saves time and reduces errors. This enables banks to focus on more complex tasks and improve overall efficiency. 3. Competitive advantage: Banks use data science to analyse the customer data and provide personalized services. The data science is crucial to the success of banks in today's digital age. It helps the banks to manage risk, improve efficiency, and provide personalized services to customers, it will contribute a better customer experience and a stronger business performance. 2. REVIEW OF LITERATURE Jaspreet Singha, Muskan Gahlawatb, Gurpreet Singha and Chander Prabha (2022) suggested that by analysing customer data, banks can offer personalized services and products to their customers. The collaborative filtering and content-based recommendation systems are effective in providing personalized recommendations. M.L Bhasin (2016) ascertained that the data science can help the banks in assessing and managing various types of risks such as credit risk, market risk, and operational risk. The machine learning algorithms such as decision trees, random forests and neural networks can be used for predicting credit risk. Serhat Yuksel, Fahrettin Ozdemirci, Hasan Dincer and Serkan Eti (2023) stated that by analysing customer data, banks can identify different customer segments and create targeted marketing strategies. The clustering algorithms such as
- 7. Banking Innovation ISBN 978-93-93996-89-3 91 K-means and hierarchical clustering are effective in identifying the customer segments. Michalis Doumpos, Zopounidis Dimitrios, Emmanouil Platanakis, Constantin Gounopoulos and Wenke Zhang (2023), examined that data science can help the banks in detecting fraudulent transactions by analysing the large volumes of transactional data. The machine learning algorithms such as logistic regression, decision trees, and random forests. It has been successful in identifying the fraudulent transactions with high accuracy. Mohsin Shabir, Wenhao Wang, Ping Jiang and Ozcan Isık (2023), found that data science has potential to revolutionize the banking sector by providing insights and predictions that were previously not possible. However, it is important to note that the implementation of data science in banking also poses various ethical and legal challenges that need to be carefully considered. 3. STATEMENT OF THE PROBLEM In recent years, the banking industry has been experiencing rapid technological advancements and innovations. These innovations have been aimed at improving the customer experience, reducing operational costs, and increasing revenue for banks. However, with the vast amount of data generated by these innovations, banks are struggling to make sense of the data and leverage to its fullest potential. Therefore, develop a data science solution will help the banks to analyse and make sense of the data generated by banking innovations. The solution should be able to provide insights to customer behavior, product performance, and operational efficiency among others. Additionally, the solution should be scalable and adaptable to accommodate new innovations as they emerge. The goal of this data science solution is to enable the banks to make data-driven decisions that will improve the customer experience, increase revenue, and reduce operational costs.
- 8. Banking Innovation ISBN 978-93-93996-89-3 92 4. OBJECTIVES OF THE STUDY A. To analyse how the data science is useful for the banking innovations. B. To evaluate the innovations available in the banking sector. 5. HYPOTHESIS OF THE STUDY a) There is no association between banking innovations and data science. b) There is an association between innovations and competitive advantage. 6. RESEARCH METHODOLOGY This study was done based on primary as well as secondary data. The Primary data was collected through questionnaire and secondary data has been collected from trend and progress of banking in India, articles, magazines and books. One way ANOVA is used to analyse the data. 7. ANALYSIS AND RESULTS a. KMO and Bartlett's Test Table: 1 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .692 Bartlett's Test of Sphericity Approx. Chi-Square 262.741 df 15 Sig. .000 The KMO test value of this study is 0.692 which is more than 0.5, it was ascertained from this study this value is acceptable. The Bartlett's Test of Sphericity is significant at 1% level of significance which shows that there is a high level of association between the variables. b. MULTIPLE LINEAR REGRESSION ANALYSIS The data science factors such as fraud detection, risk management, personalization and credit scoring are consider as
- 9. Banking Innovation ISBN 978-93-93996-89-3 93 independent variables and Competitive advantage is taken as dependent variable. Table: 2 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Durbin- Watson 1 .709a .502 .480 .748 1.611 a. Predictors: (Constant), Fraud detection, Risk management, Personalization, Credit scoring b. Dependent Variable: Competitive advantage The value of R2 =.502 which stated that the factors create 50.2% variance on the dependent factor competitive advantage. The Durbin-Watson statistics shows 1.611 it is found that there is an auto correction. The regression fit is verified with the following ANOVA table. c. ANOVA Table: 3 ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 50.201 4 12.550 22.449 .000b Residual 49.756 89 .559 Total 99.957 93 a. Dependent Variable: Competitive advantage b. Predictors: (Constant), Fraud detection, Risk management, Personalization, Credit scoring The above table indicated that the value of F= 22.449, P=.000. It was found from this study the P value is less than 0.05 therefore, null hypothesis was rejected at 5% level of significance. It was ascertained from this study, there is a significant relationship between independent factors such as fraud
- 10. Banking Innovation ISBN 978-93-93996-89-3 94 detection, risk management, personalization and credit scoring and the dependent factor competitive advantage. The influence of all the factors are estimated in the below coefficient table. Table: 4 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .810 .234 3.464 .001 Fraud detection -.062 .077 -.070 -.811 .420 Risk management .071 .105 .074 .676 .501 Personalization .022 .114 .022 .192 .849 Credit scoring .652 .069 .718 9.412 .000 a. Dependent Variable: Competitive advantage The above table indicated that the P value of fraud detection, risk management and personalization are more than 0.05. Null hypothesis was accepted for the above variables. Therefore it was analyzed from the above table there is no significant relationship between banking innovations and data science. The P value of credit scoring is less than 0.05. It is statistically significant @5% level of significance. Hence it was identified, there is an association between innovations and the dependent variable competitive advantage. 8. FINDINGS 1. Banks are leveraging data science to analyse customer data and create personalized marketing campaigns. This approach helps the banks to target the right customers with the right products and services, which ultimately leads to higher conversion rates and customer satisfaction.
- 11. Banking Innovation ISBN 978-93-93996-89-3 95 2. Data science has been instrumental in identifying the fraudulent activities in banking transactions. 3. By analysing the large volumes of transaction data, banks can detect unusual patterns or behaviors that may indicate the fraud and take appropriate action to prevent it. 4. Banks are using data science to develop sophisticated risk models that can help them to better management. 5. By analysing the historical data and identifying patterns, the banks can make more informed decisions about lending and investment activities. 6. The banks can take action to enhance their services and provide a better customer experience. 9. CONCLUSION This study concluded that the data science has played a significant role in revolutionizing the banking industry, leading to innovative products, services, and business models. The application of data science techniques such as machine learning, predictive analytics, and natural language processing has enabled banks to analyse vast amounts of data quickly and accurately, leading to improved risk management, customer targeting, fraud detection, and personalized product offerings. Furthermore, the data science has also facilitated the automation of various banking processes, improving operational efficiency and reducing costs. With the growing availability of data, advancements in technology, and increasing demand for personalized financial services, data science will continue to play a crucial role in the banking industry's future. The banks need to invest in advanced data science capabilities to remain competitive and meet customers' changing needs. REFERENCES 1. Bhasin M.L (2016), The role of technology in combatting bank frauds: perspectives and prospects, Ecoforum Journal, Vol.5, No.2.
- 12. Banking Innovation ISBN 978-93-93996-89-3 96 2. Fahrettin Ozdemirci, Serhat Yuksel, Hasan Dincer, Serkan Eti (2023) An assessment of alternative social banking systems using T-Spherical fuzzy TOP-DEMATEL approach, Decision Analytics Journal, Vol: 6, Page No: 1- 8. 3. Jaspreet Singha, Gurpreet Singha, Muskan Gahlawatb, Chander Prabha (2022) Big Data as a Service and Application for Indian Banking Sector, 4th International Conference on Innovative Data Communication Technology and Application, Page No: 878–887. 4. Michalis Doumpos Constantin Zopounidis Dimitrios Gounopoulos Emmanouil Platanakis Wenke Zhang (2023), Operational research and artificial intelligence methods in banking, European Journal of Operational Research, Page No: 1- 16. 5. Mohsin Shabir, Ping Jiang, Wenhao Wang, Ozcan Isık (20 23) COVID-19 pandemic impact on banking sector: A cross-country analysis, Journal of Multinational Financial Management Vol: 67, Page No: 1-39. 6. Paramasivan C & Ravichandiran G (2022), A Study on Technology Driven Innovation Practices in Banking Sector in Tiruchirappalli District, International Journal of Early Childhood Special Education . 2022, Vol. 14 Issue 5, p3949-3959. 11p 7.