Blog > Why AI in Banking is Needed to Tailor Customer Interactions
Machine Learning and AI in banking can create engaging digital interactive experiences!
A savvy customer may realize, AI in banking is the starting point for banks to get more relevant information. They will likely engage in more of this, and more engagement means more accurate data. This was said by Sean Connell, the CTO of Boston-based customer experience agency, Verndale.
The superpower of customer data is the ultimate key to improve Customer Experience (CX). Data around debt collection can be really sensitive, only the involvement of AI can optimize such information. Why? Unlike an AI bot, humans tend to have a biased nature that might totally crash the power of such data.
Knowing debtor habits can be helpful in several ways for a financial organization, as it can;
• Understand the credit-worthiness of the customer better
• Create a balance between human assistance and AI bot to serve customers
• Make relevant suggestions to the customer
• Automate or humanize communication-based on the customer
Let’s take a quick look some fun facts around ML and AI revolutionizing digital conversational experiences:
|Machine Learning (ML)||Artificial Intelligence (AI)|
|The progress of conversational AI is directly proportional to the evolution of ML||Your best friend might judge you but an AI Chatbot won’t|
|ML algorithms can be used to analyze the psychological traits of the customers||AI Chatbot can create engaging and friendly conversations just like humans if trained right|
|ML can be applied to understand the hidden intent in the human speech||A human can never function as an AI Bot but an AI Bot can|
We have seen banks, credit unions, collection agencies suffering from poor annual collection rate over the last few years. The primary cause of it would be:
• Struggle in providing unbiased assistance by human customer support executives
• Less human agents as compared to the number of debtors
• Delay in the decision-making process around debt collection
• Lack of predictive personalization amongst human agents
Worried? Don’t be, AI has got your back! By 2019, 40% of digital transformation initiatives will be supported by cognitive and AI capabilities, predicts the International Data Corporation (IDC). This will enable financial institutes to tailor interactions with the debtor with the help of AI in banking.
Payjo’s AI Debt Collection Assistant can identify debtor habits rapidly and improve digital interactive customer experiences, further improving the debt collection rate. To know more download the solution brief here.
1. Automate debt collection communication
AI in banking platform can easily automate communications by providing timely reminders requesting the payment of debt, sending the invoice over an email and also sending quick payment links.
2. Perform predictive personalization
It is the ability to predict the actions of the user based on past behavior. Such predictions can be really useful to tailor interactive experiences. Let’s say Debtor X likes virtual communications only, FIs can use this information to connect to Debtor X only through virtual channels to increase the likelihood of repayment of debt.
3. Identify the best time and channel for communication
AI in banking helps a financial agency to identify debtor habits, whether the debtor likes to communicate via email, phone, social media, messages and so on. Similarly, analysis of debtor habits also means knowing at what time of the day it is most suitable to connect to a debtor.
4. Access and learn from debtor history rapidly
Every interaction with a customer leaves some debtor history. Humans cannot grasp such high volume data so quickly. AI in banking platform can access and learn from debtor history rapidly improving digital experiences.
Digital interactions in a financial institution are best tailored by AI. An AI assistant will soon become a fundamental element in enhancing Customer Experience (CX). The best way to progress towards the same would be to use AI in banking to understand customer data and debtor habits.