Oluchi Chibuzor
The terms “Underwriting” and “Risk Modelling” are crucial in the retail credit industry. Retail lending refers to the practice of providing credit to individuals or entities, such as with personal loans, mortgages, and credit card loans.
In retail lending, underwriting is the process of evaluating and assessing the risk of lending money to a particular individual or other retail customers. This involves analysing factors such as the individual’s or entity’s credit history, income, and other relevant data to determine their creditworthiness and the likelihood of them defaulting on the loan.
Underwriting aims to assess the risk and in turn, determine the interest rate and terms of the loan.
On the other hand, risk modelling is the process of using statistical and mathematical techniques to predict the likelihood of future events such as loan defaults.
This includes analysing historical data, industry trends, and other relevant information to identify potential risks and evaluate their probability of occurring.
Underwriting and risk modelling services are critical to the retail lending industry as they help lenders determine a loan’s risk and interest rate and develop strategies to manage and mitigate such potential risks. This allows lenders to make informed decisions, reduce the risk of losses, and ultimately increase profitability.
Cutting Edge Solutions
With diverse solutions now available, some of the more unique and cutting-edge functionality to look out for includes Retail Modelling tools that enable the quick development of several credit risk models for your various customer segments.
These semi-automatic systems speedily develop several highly predictive retail credit risk models that are in accordance with Basel III requirements. They enable up to a 90 per cent decrease in the time required for models development and deployment.
In underwriting, very interesting are Retail Lending solutions that deliver real-time credit decisioning within risk limits by deploying a smart auto-underwriting process. These tools build dynamic rich customer risk profiles, using various internal and external data sources, without you having to write a single code.
Who Provides these Services
Bank and Non-bank credit institutions, typically provide underwriting services and carry out risk modelling using tools often purchased from specialized risk modelling firms.
These firms use advanced artificial intelligence and machine learning to achieve high levels of predictive analytics.
One of such firms is DataHarbor Africa, whose Credit Risk Analyser boasts of the rapid development and deployment of multiple credit scoring and collection scoring models with relative ease. Very unique also is its user interface which enables business users, not data scientists, to support the full lifecycle of the risk models’ development and deployment, without writing a single line of codes.
Then, there is Data Harbor’s The Builder which promises users faster and smarter credit decisioning to borrowers. In so doing, it safely scales up the retail credit portfolio, revenue and profitability. It also deploys a simple technology that frees the use of any coding. The Builder’s uniqueness also rests in its availability on both a low-cost cloud service basis, as well as on an on-premises basis. One must also comment on Data Harbor’s Next Best Offer tool that is revolutionary in providing personalised recommendations on the customer’s true credit or other needs, based on past purchase behavior. This tool boosts conversions and revenues by up to 50 per cent using its unique technology
Conclusion
With Africa’s young population and growing middle class desperate for improved living standards, retail credit is guaranteed to be a growth area. In exploiting this opportunity, however, its bank and non-bank lenders must manage a few critical issues.
These include using specific risk models for each customer segment, quick development and deployment of these multiple risk models, ensuring ease of deployment by removing all coding, ensuring high default predictability of the tools, and ensuring real-time credit decisioning for borrowers.
Continuous advancements in Artificial Intelligence and Machine Learning continue to redefine software capabilities in this area, while cloud technology and the availability of these software as a service is aiding cost-effectiveness for lenders
