Since 2018, the use of AI for the organisations have been more than doubled and 40% of financial services companies were applying AI against risk. (Birlasoft, 2019) However, not all of them see significant value on mitigating risk with AI because the advancement of technologies during that time might not be as strong as now and not all the organisations have been delivered the correct knowledge on how advanced technologies can really help them.
In the presence of pandemic outbreak, many businesses have shut down and moratoriums have been rolled out for individuals and business on loan repayments which negatively impacted the banks and other lenders business performance and being forced to be in the midst of high credit risk with increasing non-performing loan.
Therefore, this is the now or never moment to truly understand what are the must-have features before employing any credit risk management system or enhancing your existing systems to not only survive during this difficult moment, but also turning challenges into opportunities towards greater success.
The Must-Have Features to Successfully Mitigate Your Credit Risk
Leveraging on alternative data
The data source and the data points are the primary ingredients in initiating the credit risk scoring to produce a high accuracy credit risk analytics outcomes and predictions. However, you must have sufficient data before proceeding to any next step because siloed data will not reveal the secret behind your data. Thus, your credit risk management software must leverage on alternative structured and unstructured data with AI-powered data acquisition which includes internal data across lines of business and external data points from websites and mobile apps.
Online unstructured data might be challenging to capture and process but there are some software companies are now able to turn customer’s digital footprints into digital footprint scoring model. You are now able to assess to customer’s digital behaviour by analysing their activities in the digital world with RFM analysis (recency, frequency, monetary).
By leveraging on alternative data, it will not only allow you to have a holistic customer view and identify the trends and patterns, but also include external factors like market trends, political issue, economic changes, social factors to accelerate timely and precise credit assessment that provides you 360-degree view on customers’ credit behaviour and potential risk. Initiating your first step correctly and effectively is extremely important to ensure accurate predictions and help you to make better decisions towards your desired outcome.
Dynamic models (vs static model)
Static credit risk scoring model is the most common model in the market that inject static factors in the model to perform credit risk scoring without a frequent update on the latest trend and market changes. This type of model only limited to predictions of patterns and trends based on the existing historical data. Let’s take the global pandemic as an example, this external unexpected event may affect both the business and consumers financial condition due to economic downturn which resulted in higher credit risk, but the static model will not be able to react to the sudden changes on customer’s credit assessment because it only analyses customer’s credit risk based on the preset static factors that may be set 6 months or 1 year ago.
Therefore, dynamic credit risk scoring model comes into the picture to fill the gap of the static model. A hybrid model that combines both dynamic and static models allow you to be more flexible to politico-economic factors that reflects real world situations. It updates criteria within more frequent period like weeks or months, while allowing the users to add certain and uncertain factors without having to repeat the whole process. Moreover, it combines pattern from historical data but also injects with the knowledge that represents the current situation which effectively enhances the correctness of the predictions to ultimately make more accurate and faster credit approval decision.
Risk-based pricing in the credit risk scoring model
The overall objective to employ a credit risk scoring system is to rate the customer’s risk value in order to make credit approval decision efficiently. Therefore, segmentation on customer’s credit risk rating must be accurate based on the scoring value. The typical credit risk scoring model will segment the customer’s credit risk into different scoring rating, which are low, medium and high-risk customers. The low-risk customers are the group of customers that can get immediate approval while the high-risk customers should be rejected or add more conditions to get credit approval such as higher interest or lower loan amount. How about the medium risk customer? It usually requires additional time and human resources for further checking to come out with a compatible loan with more condition to get credit approval.
Nowadays, there are credit risk scoring models that even provide discriminated pricing recommendation to help financial institutions to provide the best loan solution to the medium-risk customer in nearly real-time to eliminate the additional time and effort for further checking while optimising the loan revenue by not losing the potential customers since medium risk customer might have a high chance to get rejected when further checking unable to clarify their positive credit behaviour. The discriminated pricing recommendation will be provided based on different interest rate, tenure and loan amount depending on the customer’s risk level and credit behaviour.
Main Objectives of a Robust Credit Risk scoring model
- Adaptive to situations change – In this fast-changing era, a model that is adaptive to unexpected changes is essential to ensure the accuracy of predictions and analytics outcomes so that the sudden events will not impact the outcomes.
- Reduce time to credit decision – Eliminate time for further checking on medium risk customers with the “discriminated pricing” with different interest rate, tenure and loan amount recommendation. Near real time scoring can also improve customer satisfaction, without having to wait for weeks to get the loan results.
- Ensure accuracy on credit approval decision – Leveraging all type of relevant structured and unstructured data from internal or external that provide a complete snapshot of customers’ credit behavioural and potential risk to ensure accurate credit risk scoring for a better and faster credit approval decision.
- Balance the risk and the reward – Providing loans to new and existing customers is one of the most lucrative business for financial institutions. To ensure that these loans are going to be repaid by the borrowers, having an actual prediction of credit risk scoring is important to effectively reduce non-performing loan while providing risk-based pricing (discriminated pricing) for this segment will ensure risk and reward are balanced.
The Key to Mitigate Your Credit Risk
Mitigating risk is always a crucial task to eliminate any other potential damages or losses to an organisation but it is never been an easy task to achieve the goal. With the rise of technology advancement, risk management no longer requires additional human resources or time to research and predict what may be happening in the future and what is the potential risk that may affect the customer’s credit risk. However, not all the technologies could help you to solve your problem or achieve your desired goal since choosing the right technologies is also an important stage to turn challenges to opportunity. If you are looking to mitigate your credit risk efficiently, these 3 must-have features are what you should be looking for when you are considering on evolving your organisation through digital transformation to achieve your desired outcome.