Credit unions are the new favorite of the borrowers with their membership seeing a whopping 1.1% increase in the first quarter of 2019* in US alone. However, this has also led to the increase in delinquencies, pulling credit unions back and forth in their growth journey.
As the banking landscape changes with the evolution of technology and customer expectations, it requires credit unions also to evolve in order to grow and stay relevant. Apart from developing new methods of interaction, this also requires the credit unions to evolve their collection strategies. To be able to transform their collections operations, credit unions need to make the processes more intelligent and at the same time focus on providing a great experience to their customers.
Traditional recovery methods are still favored upon by collections teams, leaving an stale experience for both the members as well as the credit unions due to not keeping up with the trends in customer behaviors and expectations. In many cases, aggressive collection strategies have also hampered the relationship with the members resulting in severe legal implications.
What if the credit unions could identify and mitigate risks? Today, there are ways to leverage predictive analytics to know if a member would turn delinquent or not. With growing business needs, challenging economic conditions and ever-evolving borrower’s profile, you need something which can dig deep into the data and provide you with insights that can help devise flawless strategies. This is where artificial intelligence makes its grand entry.
While AI and its capabilities such as ML and NLP have been leading the digital transformation for financial institutions for long now, debt collection is a relatively new area where AI hasn’t been explored much, It can however empower credit unions with its intelligent predictions and recommendations, which can help reduce risks.
AI and ML can transform the debt collection practice for credit unions in majorly two ways. One – by embedding intelligence into their collection strategies and two – by enhancing contact strategies through intelligence.
AI can help in segmenting the customers better by identifying patterns and behavior thereby empowering credit unions to recognize customers at risk of going delinquent versus non-delinquent. This helps target the right members to follow-up for debt collection, thereby ensuring smooth experience for those who pay on time. Accurate customer risk segmentation achieved with the help of AI helps financial organizations enhance customer experience with personalized collection and communication strategies. For instance, reducing outreach plans or call investments for low-risk customers will help save costs and ensure better experience. Similarly, for mid-risk customers, prioritization can be based on value at risk, call value and risk scores. For high risk customers, trying an early loss mitigation plan will help.
While it does have a lot of benefits, when an organization implements AI into its process, it is easier said than done. Credit Unions adopting these new technologies face a lot of challenges including the complexity of integrating AI application with existing infrastructure, lack of knowledge about AI/ML technology, lack of proven record of AI/ML initiatives and concerns with data quality and security. There are also many myths surrounding the implementation of AI with regards to data transfer, security and transition from old systems to new, which stop credit unions from going completely digital. Not to forget the additional woes of training, which is required to understand the digital aspects to overcome rising organizational challenges during implementation.
Amidst so much doubts and fears, there is definitely a need for a solution, which can intelligently drive the collection strategies, at a reasonable cost, while enhancing customer experience and reducing operational challenges. Also, the solution should be easy to understand, implement, secure and compatible with the existing systems, and which can help derive the desired outcome in just a few weeks.
One such solution with FinXEdge Collect, which leverages credit history, customer behavioral scores and macroeconomic factors for an accurate segmentation and prioritization of delinquent accounts. A unique and effective solution, which can accurately identify customers in different risk categories early in the cycle and follow up by creating personalized resolution strategies, considering the financial disposition and behavioral aspects of the borrower. FinXEdge can be synced with existing core collection systems and does not require additional resources for training. This works for credit unions – as they can avoid changing their existing systems and training their resources. Factors such as easy implementation and faster time-to-market, unbiased data model, unified view of customer accounts and audit ability for regulatory purposes make FinXEdge the ideal choice for companies concerned about growth and value cycles.