Separator

Digital Payments & Hyper Personalized Offers Based On Big Data & AI

Separator
Digital Payments & Hyper Personalized Offers Based On Big Data & AI

Vikas Narula, Vice President - IT, AU Small Finance Bank, 0

Vikas has been associated with AU Small Finance Bank for over nine years now, prior to which he has worked with Newgen Software and Tech Mahindra.

We are witnessing the growth of user base, transaction and ease of payments on GooglePay, Paytm, PhonePe and other digital payment mode. Users need payments to be instant, free and seamless experience. To meet these customer expectations, payment companies have rolled out new features such as biometric, fingerprint, face recognition.

While providing hyper personalized offers on Digital payments will help platforms to acquire new customers and retain existing customers. Financial institutions now deriving customer insights to target campaign specific customer segment based on their preferred mode of transaction, social and browsing history.

Hyper personalization is in its most simple existence, it’s a form of marketing in which a site takes AI or ML, audience based logic, and rea time data about a prospect and their company and uses it to tailor an experience to an extreme level.
Most hyper personalization can be done in realtime. This means that personalization software can take a visitor’s browsing behavior, content interactions, device type, and other context clues to automatically display the correct offer.

While personalization is often rule based, hyper personalization often leverages data that’s been discovered in big data sets.AI or ML personalization products can help identify these opportunities and launch hyper personalization campaigns.

There are many benefits of hyper personalization, some of are:
•High number pages conversion: When we show a visitor or customer something that tells their unique requirements and needs, something interesting will occur. Rather than having to make more attempts to sell at them or get them to use something they don’t want to use, hyper personalization allows us to throw up the perfect offer in front of us at the perfect time.

•A better understanding of our customers: On the front end, we’ll be more precisely targeting offers. On the back end, we’ll be unlocking a lot of insights about our customer base.

•Less wasted sales time: Rather than having sales team chase customers that don’t want to be contacted what if we could reach them only when they’re a good fit for your product or service?

•Trust highly: When you serve your visitors and customers with exactly what they want to get, you align
your interests. And consequentially, you start to earn consumer trust whether you’re selling to any business or customer. They’ll value your brand more precisely in long run and you’ll start showing that actually you taking care of them.

An AI system can examine millions and billions of data points and find patterns and trends that people may miss


The three main channels where banks can use artificial intelligence to save on costs are front office (Conversational banking), middle office (antifraud) and back office(underwriting).

Use of Artificial Intelligence in banks
AI has become a factual game changer in the world of bank. An AI system can examine millions and billions of data points, and find patterns and trends that people may miss, and even predict future patterns. AI, using natural language processing, can even be used to create chatty structure that customers inverse and perform specific actions using by voice application or chat. These are some use cases of AI in banking Sector.

1. Automation designed for AI in Banking
Using AI technology it's possible to automate processes for manage tasks like understanding new rules and regulations or creating personalized financial reports for individuals. AI allows bankers to make loan decisions in seconds, assessing risks and spending patternsand even looking at alternative sources of data, such as payment history of rent and utilities. Automating is use for the decision making process, bankers can reduce their risk of defaulter loans, also can improve customer experience.

2.Reporting & Analysis using AI
Some years ago, if customer wanted to check his bank balance, he had to log onto his computer visit bank's website, and look. Now using mobile banking apps and web portals, financial service AI can analyze consumer’s individual account data to see what they have how they’re performing financially, make recommendations on future actions based on the results, and then help with automation for savings and budgeting for better financial.

3.Predictive Analytics
Consumers want to be warned andreminded of important information about their own financial data not told about issues after the fact. They want to be advised when they should and shouldn't make purchases, not be sent an alert when they've accidentally overdrawn their accounts. The tool uses AI and machine learning, predictive analytics and even user feedback to predict future outcomes. It helps users make smart decisions based on their financial picture at the moment so that customer can take right decision at right time.

4.Chatbots
It in banking is not only a money saving tool, but also can automate simple tasks such as opening a new account or transferring money all basic helps related to banking. Similarly, as more and more financial institutions develop voice applications, the chatbots will need to recognize vocal pitches, inflections, pronunciations and accents.

Current Magazine

Trending Stories