5 Ways AI and Machine Learning Improve Customer Experience and Drive Business Growth

Introduction

Digital transformation has disrupted the financial services sector today, creating two worlds, namely traditional and FinTech. Increasing data-driven trends in fintech have cut across traditional financial services in banking, equity, and debt collections.

Although they are miles apart from each other in the way they approach their businesses, the end goal remains superior customer experience (CX). However, lack of clear visibility into customers’ debt, an indifferent approach to collections, stringent collection strategies, and manual collection processes are resulting in poor collection yields and higher costs to collect.

Further, the impact of COVID-19 on debt collection and imminent defaults have necessitated a digitally driven, standardized approach to ensure collection management efficiency, process consistency, and reduced customer anxiety.

Traditional Debt Collection Challenges and How an AI-Driven Digital Debt Collection Approach Can Help

The traditional approach requires associates to constantly call or email customers for debt collections. Most organizations do not have the resources to analyze their customer’s profiles, leaving associates to contact all types of customers, defaulters and otherwise, without any prior information and intimation, leading to suboptimal results.

This approach also demands that associates constantly call debtors or meet them in person until the collection is made. Overdue debts punctuated by default charges and lawsuit threats strain the relationship between the debtors and collectors, irretrievably destroying them in many cases.

Today’s digital-first customers prefer digital channels of communication. This is the right step towards digitization of the debt collection industry. Being at the forefront of this transformation, fintech companies can choose a data-driven approach leveraging Artificial Intelligence (AI) and Machine Learning (ML) in debt collections.

Here Are Some of the Ways AI Can Establish a Goal-Oriented Approach and Improve CX

1. 1

   Driving Customer-Centric Collections

   Today’s customers seek authentic and personalized communication. AI and ML provide associates with insights on customer needs and preferences to customize their communications, choose preferred channels, and determine time and frequency to reach debtors swiftly.

2. 2

   Maintaining Compliance

   Regulatory concerns have been on the rise year on year, with stringent privacy laws and loss mitigation strategies becoming increasingly integrated with lender offerings. Data backed by AI can identify the optimal strategy and engage with customers accordingly.

3. 3

   Crafting Customized Insight-Led Solutions

   AI platforms provide a 360-degree view of customer’s behavior, leveraging data from all available sources such as credit data, social profile, and financial history. This helps in defining clear category segmentation and aids in devising strategies with custom-made requests and recommendations. While in conversation, associates can have a single-view customer dashboard that helps them push relevant repayment options, recommend plans, and streamline communications.

4. 4

   Taking a Data-Backed Omnichannel Approach

   A data-backed omnichannel approach helps fintech companies create an effective omnichannel approach across multiple customer touchpoints such as emails, calls, SMS, and others. Key engagement insights lead to faster collections and reduced operational costs.

5. 5

   Improving Operational Efficiency

   Analytics on historical customer interactions provide key insights to manage real-time issues during customer interactions. Using data-driven research makes the debt collection process more goal-oriented, customer-friendly, and accelerated, helping improve operational efficiency.

Conclusion

Customer experience drives business success. Leveraging AI and ML can help improve CX and provide wider business opportunities for lenders, a major factor for fintech companies to lead the way in their adoption. However, to leverage AI and ML to its optimum and derive contextual insights, you need a robust platform that can help integrate data from traditional as well as digital sources.

Sources

Google