10/07/2020 - Indian Economy | Mainstorming

IAS Parliament 4 years

KEY POINTS

· From personalisation to customer service, fraud detection and prevention to compliance, and risk monitoring to intelligent contract documents, AI has helped banks gain better control and predictability.

· While Indian banks have explored the use of AI, it has primarily been used to improve customer experience by adding chatbots as an additional interface for customers like SIA by State Bank of India, Eva by HDFC and iPal by ICICI.

· However, reliance on legacy systems, lack of data science talent, and cost constraints have impeded seamless adoption of AI. They must focus on three key aspects:

Fraud detection:

· AI plays a vital role in fraud detection, given the heightened threat of cyberattacks. As per the 2019 RBI annual report, losses due to banking frauds have risen by a whopping 73.8% despite the Government’s efforts to curb them. What is more alarming is that banks took an average of 22 months between the occurrence of fraud and its detection, as per RBI data.

· Set against this backdrop, banks must deploy context-sensitive AI solutions to enable advanced and adaptive real-time monitoring of their payment networks. These AI solutions additionally leverage relevant data points to assess transaction risk, true identity-matching, and identification of complex typologies and patterns.

Digitisation of processes:

· The tremendous proliferation of mobile devices and the internet can be leveraged to enable the superior user experience and analytics-based functionalities that give consumers an insight into their spending patterns and provide recommendations on investment and risk profiles.

· For instance, digitising the KYC process to eliminate the need for physical document submission and verification is something that traditional banks still do not offer. This can be simplified by utilising AI-based computer vision technology to verify documents, Optical/Intelligent Character Recognition (OCR/ICR) technologies to digitise scanned documents, and Natural Language Processing (NLP) to make sense of them.

Decision making:

· AI is a great fit in areas where decisions are based on available structured and unstructured data. For example, it can help predict potential loan defaulters and offer loss mitigation strategies that will work for them. It can help determine the best time to approach a customer to sell a new product.

· AI-based smart environments can collate data from multiple sources and drive an inference and enable SMEs to take decisions. AI can also improve straight-through processing using Intelligent Automation to automate repetitive processes that need decision making.