Reduce delinquencies and w/off’s by identifying propensity to pay through ML models

Unpaid consumer debt poses a significant challenge for financial institutions, resulting in increased losses, reduced profitability, and heightened risks. Implementing a Collections Core Card can enhance financial health by improving debt recovery through data-driven processes and boosting customer engagement.

Traditional debt collection methods are often inefficient, leading to high operational costs and low recovery rates. Customers facing financial difficulties may become unresponsive or hostile, further complicating the collection process. This underscores the need for a more effective and customer-friendly solution to manage credit card debt collections.

The solution is a machine learning-based prediction framework that forecasts payment defaults throughout the delinquency cycle—from pre-delinquency to early stages (30, 60 days past due), mid stages (90-180 days past due), and late stages (180+ days past due)—to ultimately prevent write-offs.

Separate scorecards will be created for different lending products, with potential segmented approaches during model development. Financial institutions will utilize these scorecards to strategize on customer outreach, determining the optimal timing and channels for contact. This will help lower operational and recovery costs, improve collections, and enhance overall financial health.

This is for all organizations who:

1. Want to create a scorecard for the first time.

2. Want to improve existing scorecard 

1. Higher recovery rates
2. Better KP (Kept Promises) and PTP (Promise to Pay) rates
3. Lower write-offs
4. Improved customer retention
5. Optimized use of omnichannel resources
6. Decreased operational costs
7. Greater agent efficiencies

The following data is necessary for this solution:

1. Transaction data
2. Customer-level Profit and Loss (PnL) data
3. Customer device data
4. Labels indicating past anomalies in transactions
5. Existing thresholds, if applicable
6. Regulatory compliance guidelines data

1. For customer-level propensity to pay models, any supervised regression techniques can be used. Logistic regression and CHAID-based decision trees are the most widely used.
2. For portfolio-level prediction of bucket flow rates (forward flow or backward flow), Markov Chain Models can be utilized.

1. On prem ( on customer systems or on DeepQ-AI Environment)
2. On hosted cloud space ( Customer or DeepQ-AI Environment)
   ( deployment is subject to data availability in the same environment, or feasibility of seamless data transfers within secured environments)

1. Final outputs could include lead lists with customer-level scores and risk tagging.
2. Batch files, such as Excel files or flat files.
3. Outputs can be shared through API requests. By sending an API request with inputs, you can receive a response. This can be integrated into an existing application or a separate web app can be created.