These systems give their decisions on the basis of inputs like demographic, marital status, preferences, income etc. which does not analyse any real data and just targets possible customers.
Artificial Intelligence (AI) has clearly been a game-changer across industries and the BFSI sector, which a few years back was comparatively “change-resistant”, is no stranger to the charm of this technology. AI has helped the BFSI sector in becoming more efficient, more secure and less prone to banking frauds. Conversations and engagement have become more relevant and personal, which is a welcome shift from the days when query resolution required customers to visit offices and stand in queues. The rise in digital engagement has led to a rise in AI-powered solutions that are enabling customer-facing brands to be ready for the next revolution.
Here are a few ways how AI startups and solutions have played a key role in the transformation of the BFSI industry.
Personalized customer engagement
Customer engagement is a big part of the banking ecosystem. From interacting with the existing customers to approaching new leads, building engagement is one of the key focuses for the players in this industry. Conversational AI has been a revolutionary tool that has taken the level of engagement between the bank and its customers to another level by providing personalized, relevant and real-time customer service. The 24*7, quick-response service provided by these AI bots have successfully been able to decrease the friction in banking for a lot of customers. One such player in this industry is Amplify.ai. Amplify.ai has been successfully working with various banks by deploying their conversational AI for customer engagement. Their Conversational AI superpowers have helped banks in lead generation and acquiring new customers end-to-end. State Bank of India’s Conversational AI campaign powered by Amplify.ai to build deep and natural language engagement and drive user acquisition resulted in generating an engagement rate of 61.6% across SBI apps via drip message re-engagement. SBI was able to drive 1lakh+ adoption of its multiple digital assets.
Advanced Fraud Detection
Banking frauds have evolved over the years to become more and more sophisticated and harder to track. From identity theft to credit and debit card frauds, to wire transfer frauds, new techniques of scamming banks keep on emerging with evolving technology. Such frauds have a huge negative impact on a bank’s reputation and growth. Major banks across the world have employed AI and ML-based algorithms to minimize this threat. Companies like Shape Security, Vectra, DarkTrace etc. have been successfully assisting banks across the globe to track such banking frauds using machine learning. The machine learning algorithms are able to distinguish real customers from bots and detect suspicious activity before any damage is caused.
Simplifying Credit Decisions
Majority of banks today are using credit scoring systems that are outdated. These systems give their decisions on the basis of inputs like demographic, marital status, preferences, income etc. which does not analyse any real data and just targets possible customers.
Using AI and ML, banks will be able to consider every client who makes a loan application. These predictions made by the use of AI algorithms will empower banks to reduce the number of lost customers and increase their potential base. Many banks and private lending organizations have already put AI and ML at use for credit risk management. These algorithms analyze gigantic data based on previous lending operations, debts, marital status, financial behaviour and more before deciding whether to grant the loan or not. The use of AI speeds up the process of decision-making and increases efficiency. For example, ZestFinance is one company that has its own ML platform called ZAML, which helps the companies assess loan applicants with meager credit history. Many industries nowadays are using machine learning algorithms to predict risk more accurately and cut losses.
Source: Business World
