By: Syed Raiyan Ali – syedraiyanali@gmail.com, Department of computer science and Engineering( Data Science ), Student of computer science and Engineering( Data Science ), Madanapalle Institute Of Technology and Science, 517325, Angallu , Andhra Pradesh.
ABSTRACT
The banking sector is progressively embracing predictive analytics (PA) driven by artificial intelligence (AI) to improve operational efficiency, customer experience and risk management. This paper examines the fusion of PA in banking, underlining its influence on different areas such as fraud detection, credit scoring, customer segmentation, and personalized banking. By using machine learning algorithms and big data analytics, banks can predict future trends, point out probable risks and make data-based choices. In this study, a thorough evaluation of the present status and future prospects of PA in the financial industry is carried out showing how AI-powered systems are changing the field entirely.
Keywords : Predictive Analytics, Artificial Intelligence, Banking, Fraud Detection, Credit Scoring, Customer Segmentation, Personalized Banking, Machine Learning, Big Data.
INTRODUCTION
The banking sector is presently undergoing a paradigm shift in the presence of Artificial Intelligence and Predictive Analytics[1]. These technologies are not just automating traditional processes but also helping banks to make better informed decisions. Predictive analytics involves utilization of statistical techniques and machine learning algorithms to analyze historical data and predict future results. In the context of banking, it could include anything from predicting loan defaults to detecting fraudulence on transactions.
THE ROLE OF AI IN PREDICTIVE ANALYTICS
Fraud Detection
A key application of AI technology in the financial sector is tracking down scams. It is often impossible to get around the intricacies and floods of dealings in modern banks with traditional systems based on rules[2]. AI oriented predictive models process enormous volumes of transaction information in live time revealing strange behaviors connected with possible illegal operations[3]. Machine learning algorithms primarily employed include neural networks and decision trees; these help in spotting irregularities as well as minimizing instances of false alarms.
Credit Scoring
Predictive analytics play a crucial role in another critical domain, namely, credit scoring. By examining past data about borrowers, AI models can anticipate the probability of defaulting on loans. It helps banks lending decisions and also makes sure that credit goes to those who have high repayment capabilities0/0/00 0:00:00 AM. The Accuracy of credit scoring systems has been greatly enhanced by ensemble methods which combine various machine learning models together. And the below table shows the application of predictive Analytics in Banking.
Application | Use of the application |
Fraud Detection | For irregularities AI algorithms analyze transaction data. |
Credit Scoring | The loan default probability is evaluated by predictive models. |
Customer Segmentation | For targeted marketing, AI divides customers into distinct groups. |
Operational Efficiency | The allocation of resources is optimized by predictive data analysis. |
Table 1: Explaining the applications of Predictive Analytics in Banking
CUSTOMER SEGMENTATION AND PERSONALIZED BANKING
Predictive analytics helps banks to better sort their clients using indicators such as their characteristics, inclinations and previous monetary behavior[4]. By splitting up the different people using this service into distinct groups, it becomes easy for them to create marketing plans and initiatives that suit each group as per its inhabitants’ wants which leads to improved involvement from the clients involved. Consequently, it makes them appreciate what these banks do better through customizations that are seen in various products or services that are given out.
OPERATIONAL EFFICIENCY
The banking sector is also another beneficiary of AI-driven predictive analytics in their operations. Banks can align their service delivery with buyers’ demands by predicting what they will want leading to more effective use of resources thus shorter waiting periods. Similarly, AI can facilitate preventive upkeep of ATMs and other financial systems to reduce their nonworking hour hence spending less in maintaining them[5]. The below table shows the comparison of Traditional and AI driven Predictive Analytic in Banking.
Aspect | Traditional Predictive Analytics | AI-Driven Predictive Analytics |
Fraud Detection | Rule-based systems | Machine learning algorithms |
Credit Scoring | Logistic regression | Ensemble methods |
Customer Segmentation | Demographic analysis | Behavioral analysis |
Operational Efficiency | Historical trends | Real-time data analysis |
Table 2: Comparison of Traditional vs AI driven predictive analytics in Banking
CHALLENGES AND ETHICAL CONSIDERATIONS
The advantages of utilizing AI in predictive analytics are very high while some challenges and ethical issues need to be addressed. As far as data quality and integrity are concerned, it is of great importance since false information can bring about wrong projections. When dealing with automated decisions made by machines on behalf of humans, this raises questions on how private or sensitive information is handled. Banks must make sure their AI systems are open enough for everyone to see but still follow the law0/0/00 0:00:00 AM.
CONCLUSION
The banking industry is going through a major shift courtesy of AI predictive analytics thus resulting in high fraud detection rates, improved credit scoring systems as well as personalized experiences for customers while at the same time increasing operational efficiencies. In order for banks to successfully implement these innovations, it is imperative that they tackle the challenges and ethical issues that accompany them in order to avoid unfairness or inaccuracies. There’s no doubt that the next step for banks will be continuous development together with responsible application of predictive analytics[6].
REFERENCES
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Cite As
Ali S.R. (2024) PREDICTIVE ANALYTICS IN BANKING: THE ROLE OF AI, Insights2Techinfo, pp.1