5 Pillars for Strengthening Risk Management in Digital Lending
When it comes to digitization, banking always acts as a slow moving tortoise in the race of technological switch. Banks are compelled to focus much towards digital lending journeys & its simultaneous operational tasks. As most of the industries are undergoing digital revolution, the amount of data generated is huge with multiple interactive devices.
Only recently, the financial sector has started adapting digital moves as a new challenge impacting on the reduced operational costs along with intelligent data analytics for initializing smarter digital LOS. Such smart technologies uses data for making the risk modelling system more consistent, compelling & predictive.
Such risk modelling system helps in reducing the credit risk while simultaneously identifying the suspicious transactions.
The key pillars to strengthen a successful risk modelling system implementation in digital lending journeys are-
1. Process automation- Implementation of robotic process automation through robotic underwriting helps in increasing accuracy along with cost effectiveness & time management.
Machine learning helps in reducing efforts for lesser value added processes by implementation of automated workflows. Such smart workflows helps in designing delightful digital lending journeys for retail lending as well as corporate lending customers by combining sequence of pre-defined algorithms resulting in seamless processing of digital LOS.
2. Decision Analysis- Well implemented risk modelling system assists in detecting suspicious financial activities at a very initial stage of digital LOS by observing past transactional history. Manual errors get by passed with decision automation in real time processing. Business rule algorithms helps in automation of valuation process.
3. Developing risk ecosystem- Regulatory burdens is one thing which needs to be deeply taken into consideration while designing a risk assessment model. As financial decisions needs to go under numerous regulatory norms, it is important to get the system integrated & partnered with external agencies.
Efficient data aggregation through internal & external sources helps in taking real time decisions as per the laid compliance through robotic underwriting process.
4. Data Management- Intelligent risk modelling system with improved data governance helps in making more consistent business decisions with higher responsiveness to the available risk data.
Creating & capturing valuable data insights with consistent processing & operational models for both structured (transactional data) & unstructured (e-mails, social media, text messages) data have a higher impact on risk assessment process. It further helps in designing seamless digital lending journeys for both retail lending as well as corporate lending.
5. Credit assessment approvals- Real time customer analysis leads to automatic triggering of ratings based on external credit agencies. Efficient deployment of risk modelling system helps in reducing manual tasks with pre-defined business logic.
Also, availability of visual designers ease out complex calculations related to financial ratios, logs etc. With the help of efficient risk modelling system, comparative analysis of the existing business with the set benchmark can be intelligently done for a systematic corporate lending approach.
Risk management is the new digital methodology for forecasting & analyzing the risk & suspicious account lending. It rather needs to be handled with greater care rather than quick adaptation. It is important for banks to run both the new as well as old processes to run simultaneously for both retail lending as well as corporate lending till the time it is as per the regulatory norms with efficient risk model implementation. Being slow & steady with the risk models would ultimately help the banks to win the race in the digital era.
About ORIGINATIONNEXT:
ORIGINATIONNEXT risk assessment platform represents strong risk management by creating tighter integration between model developers, model risk management & business teams. It provides intelligent robotic automation with upstream & downstream processes, risk scenario analysis, stress testing & other regulatory compliance.
