The credit risk management lifecycle is continuous. We have broken the lifecycle down into five major phases that we help clients with: loan origination, loan administration, loan automation, loan funding and credit risk. We have decades of experience helping clients improve their existing processes and helping them to automate manual decisions and even use Machine Learning and AI to make smarter decisions. Contact us today to see how we can help your business.
Loan Origination
A credit relationship begins with origination. Getting the loan origination decision right, and delivering it quickly, is critical to a successful and profitable relationship. Consumers have more credit choices than ever, so differentiating with a faster, more streamlined origination decision has a strong ROI. Effective automation delivers real-time responses to customers while ensuring that credit risk and capacity are considered, and all regulatory compliance enforced.
Check out our Loan Origination page here.
Loan Automation
The loan origination decision is part of a broad and complex process. Automating the whole loan origination process requires both effective decision automation and a modern Loan Origination System like our partner Inovatec. Taking advantage of the LOS, customers can ease the integration of bureau and alternative data sources, handle customer paperwork and support the whole customer-facing loan team.
Decision automation allows for rapid pre- bureau auto declines to keep unsuitable loans out of the process. Once the LOS has pulled together the data you need, advanced auto-adjudication becomes possible, driving to ever-higher rates of straight through processing without compromising on your risk strategy. Lender-specific policies about credit, loan to value or debt ratio calculations can be consistently enforced. Decision automation can identify key stipulations regarding documents and proof required, offer pre-approval of secured loans before assets are selected, prevent gaming by customers or dealers, handle multiple channels and blacklist bad actors.
Click here to learn more about how they have helped North American auto lenders.
Loan Funding
Once loans are approved, your LOS supports the gathering of documents and signing of paperwork. As this step processed, decision automation can check that all the stipulations have been met. It can evaluate the documents received against pre-approval to ensure conditions are being met. When valuations and other time–consuming steps are completed, it can re-check everything to make sure nothing has gone out of bounds. And if things have changed, it can even re-approve or re-stipulate automatically. When all the I’s are dotted, the platform can disburse the loan.
Loan Administration and Servicing
Disbursing a loan begins a new customer relationship. As that relationship evolves, a decision engine can identify cross-sell or up-sell opportunities and increase the ROI on your customer portfolio. (Check out more about creating a Next Best Offer here.)
A decision engine can also act as an early warning system for defaults by assessing a wide range of possible data about the loan or borrower. It can decide if a change to collateral value or credit status or anything else is material and needs action.
When an account starts to get into trouble, the decision engine can identify the best strategy for an at-risk loan – whether that’s waiting for self-correction, prompting via various channels, or moving to collection and treatment optimization. Throughout, a decision engine can segment customers and ensure they get personalized communication and plans for debt repayment.
And the data from loan administration and servicing can be fed back to improve the origination decisions you make, closing the loop and driving continuous improvement.
Credit Risk
Automated Credit Risk strategies began to emerge in the 1970s and 1980s. Back then, decision trees were the standard automation approach. Combined with procedural rule flows, if-then rules and scorecards, increasingly complex credit risk strategies were implemented. It became clear that these approaches didn’t scale well and that using them to automate a modern credit risk strategy was complex, fragile, and expensive.
In recent decades, decision models and decision tables have established themselves as the best approach for managing complexity in decision automation. It’s time for credit risk organizations to adopt decision models and decision tables and refactor their aging decision trees.
Modernizing Credit Risk (Whitepaper)
Three Ways to Protect yourself from the Next Banking Crisis (Webinar Replay)
How Decision Modeling Reduces Complexity in Regulated Industries (Webinar Replay)
