Debt collection is a delicate undertaking for lenders as they tend to dabble between debt recovery and retaining customers. Perhaps the most challenging function in the lending ecosystem, debt collection has immense scope for improving its processes. The current challenges with debt collection are:

Incomplete or old address information of the debtors
Reachability issues due to inaccurate addresses
Identifying which defaults to pursue in-house and what to outsource

With location data and intelligence backed by ML capabilities, businesses can substantially ease out their collection models on various fronts such as:

Address parsing to check the authenticity and completeness of an address
Address correction to a level via validations
Identifying pursuable defaults based on the location attributes of previous successful collections
And of course, more accurate credit risk prediction to eliminate high-risk leads at the source.

Table of Contents

Address Validation and Correction

A common roadblock is when the address of the debtor is either incorrect, invalid, or not updated when they moved to a different location. In such scenarios, it is difficult for the lender to visit the debtor.

We check the sanctity of each address, flag the incorrect or invalid address entries, and also correct them to a certain level based on the kind of error in that address.

When you know which addresses are incorrect and unreachable you can save effort in pursuing those debtors, and probably outsource collections for such debtors.

Identifying Probability of Recovery

Taking the past successful recoveries as the basis to train the ML models, we discover the location attributes associated with the addresses of debtors that repaid, and the ones that defaulted.

Once these attributes are identified, the model highlights the new addresses that have look-alike attributes. These are the addresses of the debtors that have a higher probability of repaying and lenders can pursue these collections. The addresses that depict attributes similar to the defaulters’ addresses could be outsourced.

Location data and intelligence is at the heart of solving some of the major business challenges of the fintech sector. Credit risk prediction, fraud prediction, lead prioritization, and as mentioned above, debt collection are some of the biggest challenges that real-world data with location AI can solve.

For more details on how our ML models and data algorithms work, please get in touch with us at hello@geoiq.io, or visit our website https://geoiq.io/.