If Artificial Intelligence has affected one industry more than some other, it is the Banking business. As per the recent survey by a leading global management consulting company, banks that implement AI tools could help boost their revenues by 34% by 2022. Another survey of 100 top bank executives revealed that 68% of the respondents believe that AI will improve productivity.
Following are the major use cases of AI in banking:
- AML Pattern Detection
Anti-money laundering (AML) alludes to a set of procedures, laws or directions intended to stop the act of producing pay through unlawful activities. Much of the time, money launderers conceal their activities through a progression of steps that the money that was originated from illicit or dishonest sources are earned honestly. The vast majority of the significant banks over the globe are moving from rule based software systems to artificial intelligence based systems which are progressively vigorous and keen to the counter anti-money laundering patterns. For HSBC, the number of investigations reduced by 20% without actually reducing the cases referred for more scrutiny.
2. Chat bots
Chat-bots are automated chat systems which reenact human talks with no human intercessions. They work by recognizing the unique situation and feelings in the content talk by the human end client and react to them with the most fitting answer. With time, these chat-bots gather different measures of information for the behavior and habits of the client and learn the conduct of client which serves to adjust to the necessities and states of mind of the end client. In early 2018, the Commonwealth Bank of Australia (CBA) launched its in-house bot Ceba which was able to perform 200 tasks. Nina, AI chatbot of Swedbank, achieved a first contact resolution rate of 78% in the first three months.
3. Algorithmic trading
Reports guarantee that over 70% of the exchanging today is completed via automated artificial intelligence systems. The majority of these multifaceted investments pursue diverse procedures for making high frequency trades (HFTs) when they recognize an exchanging opportunity dependent on the sources of info. Swiss bank UBS last year launched a new AI system developed jointly with Deloitte identifies clients’ requests by scanning through their emails; it reduced 45 minutes tasks to just a few minutes.
4. Fraud detection
Fraud detection is one of the fields which has gotten huge lift in furnishing precise and predominant outcomes with the intercession of artificial intelligence. This is one of the key regions in banking segment where AI has exceeded expectations the most. JPMorgan Chase’s contract intelligence (COIN) has added to the speed at which legal documents are analyzed. COIN reduces the review time of 12,000 commercial credit agreements per year from 360,000 hours to just a few seconds.
5. Customer recommendations
Recommendation engines are one of the major contributions of artificial intelligence in all the segments and banking sector is being benefited the most. It depends on utilizing the information from the past about clients as well as different offerings from the bank like MasterCard, venture strategies, reserves, and so on to make the most fitting suggestion to the client dependent on their inclinations and the clients’ history. Recommendation engines have been fruitful and a key part in revenue growth achieved by banks.
“Machine intelligence is the last invention that humanity will ever need to make.” -Nick Bostrom