Pronounced Applications of Artificial Intelligence in Retail Banking

A variety of business processes under the umbrella of retail banking are the constructive consequence of AI and automation services. Not only the payment processing automation and fraud detection but banks are also getting benefitted by automated credit scoring and customer service chatbots.

Fraud detection, credit scoring, and chatbots turn out to be the major beneficiation of the retail banking system.

How AI Detects Fraud through Fraud Detection Process?

The banks install anomaly detection software to their system which is trained in real-time on a range of labeled data retrieved from transactions and loan applications. The ML algorithms analyze every single bit of data before it can be labeled under fraud case.

If any credit, debit or charge differs from that of the predefined case which software considers normal, will be notified to human employee in the chain. Then the human employee will be responsible for accepting or rejecting the notification.

Consequently, the software will get feedback if the transaction was considered to be as fraudulent.

The software application is also capable of judging if the customer is present at the location from where the transaction has been done and hence in any disparity flags the events.

Even using the data from the loan application, anomaly detection can recognize the fraud through analyzing any falsification in the details provided by the customer at the time of application.

How AI Determines Credit Scoring through Predictive Analysis?

Other than fraud detection, AI also privileges banks with credit scoring automation. Specifically, Predictive analytics technology can accurately assist retail banks in estimating the risk of a potential customer. To determine any customer's credit score, the application can utilize data from sources outside of traditional credit and financial history.

The software would run data the bank has in the form of algorithms and further calculate the risk probability the bank possesses in case of choosing to underwrite them.

To widen the range of data types application can use for credit scoring would include social media posts and engagements. The application can also predict if the customer will make payments on time for a loan or not, just by observing their online behavior.

Extracting data from social media posts would require natural language processing software. This could recognize the content within those posts and determine whether their sentiment is positive or negative. The sentiment of the post becomes a data point which the predictive analytics software can then factor into its credit scoring calculations.

How Chatbots Enhance Customer Experience through NLP?

A considerable number of retail banks employ customer service chatbot to their digital platforms so that customers can navigate through with ease without any friction. Customers can check their balance across multiple accounts, a requirement for loan approval or how to cancel a debit or credit card.

Natural Language Processing (NLP) is the technology on which chatbots run to determine the reason behind a customer's question in real-time. A training of machine learning model on banking terms is required. Using the ML technology chatbots are free to improve themselves continuously if they have flagged any question to human employee previously.

With the advancement of AI, banks can revamp their fraud detection and predictive models to generate great business benefits while entertaining customer with demanded services.