Artificial Intelligence (AI) had been in India since long, where the universities and research institutes had been trying to explore the development of this technology, to attain some maturity. In recent years, industries have started using this technology in their business improvements. Banking and financial industries are one of the frontrunners and interacting with the AI systems of banks run parallel in lives of the customers.
The attractive economy
The potential of AI is accepted to be huge. The global spending had touched $5.1 billion in 2017 that is far ahead in pace from $4 billion up to 2015. $25 bn is the projected figure for 2024.
What values does AI bring in the Banking industry?
The biggest area of focus is the personalization of experience for users, prevention of fraud and operation support systems. These are three areas that need automation for cost-cutting and higher efficiency. Customers love personalization and trust. A well-created personalization experience and trust about prevention of fraud itself will make a bank gain more customers, enabling the bank to succeed more in their core business.
2. Customer Support:
A good customer support is someone always looks for. With AI driven automated customer support, the efficiency of customer support can be increased, the resolution of complaints and issues can be driven faster, the information needed to the customer can be provided in a more specific way and overall cost of support can be reduced. Customer support does not limit itself to just banking and customer support is not the only area that involves personalization of experience. The historical data of the customer’s preferred payment modes at different checkout points can be used to create personalized payment option for each customer. This can range from better EMI options, instant net banking or card checkout options etc. An AI advisor bot can lifestyle, risk appetite and expected returns of each customer and come up with personal investment planning.
3. Reduces Manual Work:
Humans spend a lot of time in back-office operations. They do a lot of errors too. This involves the manual reading of paper-forms, data entry, and relationship manager operation planning etc. All of them can be automated. For instance, the reading of paper-forms can be delegated to OCR reading which will also insert data into the system.
4. Fraud Detection:
Massive monitoring of credit card uses can be carried out in parallel in real time to detect anomalies and therefore prevent fraud, for example, declining a suspicious transaction attempt. Security breaches can be predicted from logs and emails over the bank’s network. Frauds can happen at ATM points. With good image/video analyzer AI, keypad interaction pattern identifier AI, it can be found in real time if the authorized user of a card is making an ATM transaction and thus decision upon transaction can be taken. The video analyzer can also be trained to take a decision in identifying ATM security breach and thus security personnel can be deployed at the moment.
What are the Challenges?
The forecasted growth in the economy due to AI in banking and seamless use of AI in banking can be realized after overcoming two main challenges.
Since AI is largely data-driven, the first challenge is about the truth of data. Data can be forged manually to make the Artificial Intelligence take a favorable decision.
Data can also be in a bad shape because of data collection method or data sources. Therefore the integrity of structuring of data and sourcing out true data all the time is a challenge. AI is a disruptive technology and a large number of people want to go for it, seeing an attractive career. Because of the rush, there are many people available but there is a shortage of skilled people who can make the best AI systems. To match the pace required for AI-driven economy, we need more people who can make the best AI systems.
AI is not just adding funds to the industry, but also to the segments which are training people on AI