Addressing the Challenges of the Lending Industry
Banks and commercial lenders have learned hard lessons recently about the inherent risks involved in underwriting loans. Historically, these lending institutions have depended, in large part, on credit scoring methods to determine risk levels and to make their lending decisions. They have now come to realize, however, that credit scores don’t provide a complete picture. What’s needed is a broader and deeper set of data that incorporates, amongst other factors, rapidly-changing market conditions – and an automated means to monitor this dynamic risk scenario in real time.
Cognika Solution
Cognika’s smart analytics solution — Cognika ESP — combines advanced machine-learning (AI) technology with unique human cognition-based algorithms to automatically generate risk models from large, multi-dimensional datasets. When formulating their lending rules, banks and commercial lenders can use Cognika’s predictive risk modeling functionality to:
- Automatically capture and analyze the whole risk scenario, including personal credit history and changing market conditions
- Easily incorporate early-warning triggers with automated “alerts” to eliminate surprises and respond quickly to potential problems
Using Cognika ESP, lenders can model risk more accurately – and more confidently.
Cognika’s technology could also be applied to broad-based consumer marketing businesses that generate both fee and asset outcomes (credit cards and small business lending). The application of Cognika to risk underwriting systems would seem to favor large volume, smaller ticket business – e.g., underwriting for short-term lines or for small ticket leasing. The application of Cognika to structured finance would be leading edge, for example, in CDOs, CLOs and SIVs. Models that are purely emergent from data are ideally suited to modeling of trading data for money managers.
Cognika’s advanced technology can also be applied effectively at insurance companies to increase levels of accuracy when developing risk models for asset-based securities. The Cognika ESP system can be used for decision support during evaluation for acceptance and selection of appropriate terms to be offered to new members. The predictions are based on matching the profile and available information of the applicant with the historical information used as a learning data set by the system. In the diagnostics model, Cognika ESP provides insights into factors that influence different outcomes of interest. Cognika models can also be used by insurance companies for prediction and simulation for decision support and strategic planning.