Loan origination systems are being reshaped by Big Data and predictive analytics, forcing banks and alternative lenders to compete on a new level. To outpace competition and maintain a leadership role, lenders should leverage decision automation systems in the most robust way.
Experience of our clients and our research of most strong challenges in decision automation systems reveals a list of the seven necessary elements of a robust decision automation system for lending.
1. Visual editor for interacting with decision flow
Loan underwriting processes are growing in complexity. To successfully navigate the challenging journey of building you first custom decision flow for loan origination process, you need a genuinely smart, simple and helpful visual editor.
2. Robust integration of multiple decisioning strategies
The world around is changing and so does the business flow and lending policy. Non-stop improvements in the decision flow has shifted from the frontiers of innovative lending to a mainstream necessity. A truly flexible and robust decision automation platform should provide you with the capability to manage multiple decisioning strategies and running them simultaneously, and designing the optimal decision flow using champion/challenger approach.
3. Seamless integration with the third party data
Big data provides huge advantages to lenders if they can transform their knowledge into profitable customer acquisition decisions. When evaluating the capabilities of a decision automation system make sure it can not only source third party data, but also convert raw data into accurate, business-driven analytical models.
4. Enhancing decisioning with analytical and scoring models
In his recent interview on the future of data mining Gregory Piatetsky-Shapiro, editor and chief scientist of the leading data mining digital publication, KDnuggets, said: ” I expect that techniques and algorithms will become better adapted to real-time embedded analyses out of the box.”
Custom prediction models and scorecards enable informed, fact based decision making on every stage of the business flow. An efficient decision automation system must go beyond allowing you to upload scoring models in PMML format. Ideally, users should also be able to monitor performance of their statistical models, adjust, calibrate and update them as needed.
5. Framework for managing in-house scoring models
As banks step into a Basel III domain, supporting Internal Ratings Based systems becomes one of the top features of a loan origination system. Designing and improving internal scoring models allow executing more detailed risk assessments of potential borrowers and greatly enhance risk and capital management capabilities.
6. Balance between human intelligence and automated actions
Decision automated platform should optimize staff efficiency by presenting users with a data-driven understanding of the consequences of every possible action.
The perfect loan origination system will simplify data interpretation, extract insights and visualize data and thus deliver better decision-making to every person involved into the loan origination process.
7. A mechanism for flexible connection of those involved into the decision making process
Streamlining decision flow means maximizing collaboration efficiency across business units and between front office, analytical team and the executives. As volume and variety of automated actions increases, it is important to maintain transparency in the way that users interact with each other and with the system.
In order to develop an agile, analytic and adaptive business, lenders should ensure rigor and balance in decision making. These seven features are building a simple and clear framework to assess different decision automation platforms. What metrics have you been using to evaluate a decision automation platforms?