View: Indian banks need to get much smarter

Even though artificial intelligence (AI) is used in all sectors and disciplines, it is the bank that has adopted it faster than most other sectors. Today, many banks are using advanced AI chatbots to resolve customer queries and provide better services. Some use robotics software in their trading processes in various functions, thereby processing a large percentage of transactions with greater accuracy. In the area of ​​early fraud detection, AI is also extremely helpful.

Visual identification and verification is another important area where banks are benefiting from AI, especially when it comes to insurance claims. Geolocation can help more banks target their customers at a granular level with a high success rate.

Indian banks nowadays make more operational decisions through machine learning (ML), mainly in automated loan processing, customer acquisition, credit monitoring, customer retention, cross-selling , upselling, churn analysis and chargeback forecasting. Personal savings and wealth management, based on transaction history and customer behavioral data, is an area where likely expenses can be predicted and therefore the bank can prescribe savings and investments ideals to its customers.

Implementing AI will result in lower cost of acquisition, lower probability of default, stronger customer activation, higher product depth, lower churn, and better compliance. All of these will help banks engage with their customers from acquisition to retention and thereby create value for the organization.

Even though the banking sector is ahead of other sectors in terms of analytical maturity, there is still enough room for more innovation. Early adopters of AI can gain significant advantage and experience. Despite billions of dollars spent annually on technology upgrades around the world, few banks have succeeded in scaling AI technology across the organization. The main limiting factors are the lack of a clear AI strategy and the required infrastructure.

For this, an AI-based approach is needed. This will create a “smart bank”, where most business decisions will be made through a mix of human and artificial intelligence. As a first step towards smart banking based on core competencies, size, business complexity, risk appetite and degree of aspiration, banks must first design their own strategy, areas where AI should and should not be adopted.

All low-end back-office based processing jobs, for example, can be automated, saving human capital and improving efficiency with reduced turnaround times. These back-office resources can be reallocated to multiple other activities. At the front office level, in areas such as marketing, new business sourcing, delinquency prediction, and churn prediction, a bank can use its AI capabilities wisely whenever needed based on their skills, tools and techniques required.

As many banks in India have limited IT infrastructure and specialized skills for advanced analytics, banks could partner with fintech companies, where banks will provide their domain expertise as well as their broad base of data, while fintech will provide its latest technology. However, banks need to achieve speed, agility and flexibility with fintech. They need to invest adequately to strengthen their core technology to support various analytical tools. Data purity is another important area. Appropriate skills to convert raw data into meaningful results are a priority requirement.

Emerging technology regulators will need to take a proactive role in crafting appropriate regulation to balance the commercial interests of banks without compromising the privacy and security of customer data. The future lies in analytics, where pioneers will have the comparative advantage to improve their competitiveness and profitability by offering smart banking services.