Our multi-algorithmic, time-series forecasting solutions provide granular forecasts of product demand, quantities, and revenues with an average accuracy of over 90%.
Multi-modeling technology accounts for multiple trends within the training datasets to produce highly accurate propensity ranking scores.
Findability Sciences' AI model helped Alliance One achieve a 40 % ROI
Alliance One achieved 20-30% year over-over-year uplift in collections consistently over of two years, resulting in a 40 % financial ROI for 2021. Findability Sciences helped them formulate an AI-based propensity score model to prioritize and call the most likely payers first. This partnership resulted in increased revenue for Alliance One and measurable financial impact for their customers.
Global debt collections agency
No incremental staffing cost was involved
Yield of dues collection improved from
24% to 30%
Overall improvement of debt collection moved to
A global debt collections agency wanted to improve payout rate of overdue invoices for its stores in the US. The client wanted to improve upon the existing dues collection rate (24%) through a renewed debtor contact strategy.
We proposed an operational strategy of ranking each invoice across a debt prioritization matrix based on the outstanding invoice amount and probability of payout.
A proprietary self-learning, multi-modeling automatic predictive technology from Findability.AI was used for obtaining probability of payout.
Global accounts receivable management company
Retailer's debt collection increased by
observed during the measurement period
Collection partner's fee revenue increased by
Additional manpower cost was involved
Global accounts receivable management company wanted to optimize the collection of overdue balance from loyalty program members. Around 25000 customers accrue a cumulative debt between 4-45 million USD in overdue payments every year.
A proprietary self-learning, multi-modeling automatic predictive technology from Findability.AI was used to assign a 'propensity to pay' score to each record in the debt portfolio.
The debt portfolio was divided into 7 'age buckets' defined by the debt collection partner.
Debt collection increased by
witnessed by end customers
without additional staffing cost
Refreshed results every week
received by client to take timely action against potential default or propensity
Due to increasing competition, it is necessary for telecom carriers to maximize dues recovery as well expand their subscriber base. A balance between soft action like sending reminders and hard action like discontinuing the service is important for collection teams.
'Propensity to Pay' was calculated using a proprietary self-learning, multi-modeling automatic predictive technology from Findability.AI.
Overdue accounts were divided into deciles based on the decreasing order of 'Propensity to Pay'. The accounts were also monitored for inter-decile shifts and to detect steep changes in propensity to pay if any.
Default risk reduced
due to proactive monitoring of loans at risk of default
There was no incremental staffing cost
Follow-up and collections strategy
could devise by operations team
by likelihood and amount of charge off
Defaults in retail loans and resultant charge offs are an important cause of credit costs in banking. Along with loan defaults at portfolio level, it is also important to identify the individual loans at higher risk of default to effectively target credit risk mitigation efforts.
A proprietary self-learning, multi-modeling automatic predictive technology from Findability.AI was used to divide the loan records into deciles based on their likelihood to default.
The proprietary algorithm ingested historical data to develop multiple prediction models to account for all possible scenarios in the dataset..