In less than a decade, the two important D’s, i.e. Data and Digitization have changed the face of the Indian banking system. The millennial generation is becoming a significant customer base of banks. Catering to their banking needs demands more than average human capabilities. And Artificial intelligence in banking is a key to this wave of transformation.
Artificial Intelligence In Banking
The technology that enables machines to simulate human intelligence and thought processes is termed Artificial Intelligence.
Opportunities For AI In Banking
AI has a great potential for banking technology to move beyond the brick-and-mortar banking system. The use cases of AI in the banking sector can be overwhelming.
1. Anti-Money Laundering (AML) And Fraud Detection
AI can help monitor and prevent suspicious activities like security breaches, frauds, money laundering, and potential risks.
Transaction monitoring with AI-based graph analysis can unearth non-obvious connections among individuals in the transaction chain. AI-assisted analysis of government documents can help extract shareholder information to establish Ultimate Beneficial Ownership.
Entity resolution with AI is the best AML method for a single unified customer view across national and international databases.
Finally, AI can assist in adverse media monitoring to search for negative news about a client by categorizing new articles, criminal proceedings, and generating relevance scores.
2. AI In Banking For Backend Processes, Automation, And Digitization
The use cases of AI-assisted backend processes and automation are plenty, with everything from customer onboarding to compliance monitoring to writing investment reports to credit scoring to reconciliation.
AI bots can help take up numerous manual tasks with enhanced accuracy, improvement in processing time, reduction in response time, and cost-efficiency and frees the employees for more skilled work.
AI-assisted digitization of KYC processes can eliminate cumbersome steps like physical document submission and verification.
3. Personal Finance And Wealth Management
Today customers want banks to take an active interest in their financial well-being. With the help of specialized AI bots for personal finance management and wealth-building, banks can establish a hand-holding approach that gives real-time insights to customers about market risks, expected ROIs on funds, investments, etc.
Imagine this, banks mining transaction data to forecast what your spending will be like and telling you- “Your grocery expenditure will increase by $70 this month” and then offering advisory services like – “Limit eating outside.” An auto-saving feature may even tell you- “Moving $50 to savings this month” based on your forecasted expenses!!!
4. Loan Decisions And Credit Scoring
Banks can harness AI algorithms to calculate credit scores by drawing on an individual’s banking transactions, spending and earning patterns, past decisions, familial history, mobile data, etc.
AI in the banking sector can help access borrowers’ information from different sources on the internet and assess creditworthiness for wiser credit decisions.
5. Customer Insights For Personalization
Banks can utilize AI algorithms with their cognitive engagement solutions to anticipate customer needs and offer actionable insights. It facilitates the delivery of hyper-personalized services and products.
Challenges Holding Back AI In The Banking Sector
Although there are many interesting use cases around fraud analytics, credit underwriting, and customer personalization, Indian banks are still in the fetal stage in exploring artificial intelligence in banking.
So What Are The Challenges Of Artificial Intelligence In Banking?
1. Lack Of Skills In The Existing Workforce For Advanced Tools Of AI In Banking.
There is a surging demand for skilled domain experts and data scientists to use the data in hand with a more accurate and credible approach.
2. To Deploy AI, Banks Must First Match The Government’s Tighter Compliance And Regulatory Scrutiny.
An increase in automation would explicitly increase risk exposure. Hence, net banking and online transactions must come under the radar of stringent privacy regulation policies.
3. Currently, Erroneous Banking Databases Are Posing The Biggest Challenge For Using Artificial Intelligence In Banking.
AI intensely depends on the quality of the data, and if the data itself is inaccurate, then a successful AI implementation is far from reality.
4. Data Silos And Legacy IT Infrastructure Pose The Biggest Threat For Banks Going Forward With AI.
To build scalable and competitive AI models, banks must tear down data silos through effective communication between different systems and departments and seamless data integration.
5. Most Banks Have Trouble Figuring Out What AI Can Or Cannot Do For Their Respective Systems.
Because reports around AI are polarizing it either as a cure-all for the banking problems or as something that will put us out of our jobs, AI is subject to hype-driven goal setting by banks.
In A Nutshell!
While competing with big techs and Fintechs, banks must be relevant, appeal to changing customer behavior, offer personalized banking experience, improve margins and gain additional revenue. Thus, Indian Banks must overcome the challenges of artificial intelligence in banking and must embrace AI.
