• Divya Ahuja
• 19^th Apr, 2021

"Fraud detection in Banking Industry" - The new era of AI-Drive

The AI in emerging market reveals that 85% of all respondents currently use some form of AI to boost speed and efficiency, with 77% saying it is one of their most important investment areas going forwards.

• Fraud detection has been one of the major challenges faced by all banks and financial institutions. Such frauds deeply impact bank operations, their capability to grow, and maintain profitability. It also impacts the reputation of the bank for its existing and new customers.
• With the rapidly growing banking industry in India, frauds in banks are also increasing amazingly fast, and fraudsters have started using innovative methods. These bank frauds are becoming more and more common in the digital platform leading to many fold losses for banks and slowing their growth.
• In real-time effective detection and deterrence, fraud strategists are seeking a holistic view of the threat landscape and adopting a multi-layered defence system for a balanced strategy However, existing detection systems depend on defined criteria or learned records, which makes it difficult to detect new attack patterns. Therefore, various methods using machine learning and artificial neural networks have been attempted to capture new financial fraud.

Banks and financial institutions are inherently vulnerable to fraud and scams, which is why being able to detect as digital banking apps and online spending continues to grow, so must the efforts to detect and prevent fraud.

(a) Research on the various research papers based on the machine learning and artificial neural network techniques and review of latest detection techniques mainly from 2017-2020

(b) Analysis of advantages and limitations for the latest research paper

(c) Model building based on the implementation of reviewed paper and full process experiment based on actual financial transaction dataset

(d) Deriving the result in specific way through validation on each step in the process

(e) Comparison with traditional machine learning and deep learning based on artificial neural networks for fraud detection.

Banking institutions should ideally build or integrate advance models that help predict future instances of fraud to proactively catch irregularities.

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