Deepaiy predicts the number of days a credit is exposed.
The impact of accurate predictions on human decisions has a great extent: for example lower cost of funding, lower NPE rates, improved pricing of risk, earlier warning signals, accelerated collection.
Deepaiy AI/ML proprietary models process the debtor cash-flow enriched with small context information and public data. The data mart is segregated and fully owned by the client.
In each run several AI/ML algorithms compete to perform the most accurate predictions. The top performer is chosen based on six back testing metrics.
The client joins the Deepaiy Data Pool (DDP) to overcome the scarcity of data. The data are collected anonymously and the entity is masked to safeguard competition.
Deepaiy predictions can be embedded in client's systems or published on the web Business Intelligence interface, personalised with client's metrics.
Why is Deepaiy Data Pool (DDP) different?
1. Small Data Issue
The clients with small data join the Deepaiy Data Pool (DDP)
The data are submitted at entity or business line level
Before sending the data, the debtors are masked and the entity is anonymized
2. How DDP works
A and B are both creditor of D and join DDP
D doesn't fulfill the obligation with A
In a standard data pool, B has an advantage thanks to the information submitted by A
In DDP D is masked and the creditor A is anonymized, so B doesn't have a direct advantage
3. Central Agent
AI/ML proprietary models are trained on the central DDP
The trained models are called Central Agents
The client data is submitted to the Central Agents to perform more accurate predictions