What does data mean to your business credit risk? Is it a source of insight used to inform better credit risk management, or a constantly building burden that threatens to overwhelm your risk analysis team.
The question of how to mitigate risk is central to avoiding bad debt, protecting revenue, and ensuring appropriate credit risk modeling. But in a dynamic and increasingly connected digital landscape, that means understanding the value of data, and leveraging that data to drive better risk decisions.
Data is fundamental to effective, next-generation credit risk management. It provides a path to more efficient and accurate decision making that reduces enterprise risk. And thanks to the power of artificial intelligence (AI) and machine learning (ML) technologies, it can deliver on that promise in real- or near-real-time. But without the right data, and robust data management, that potential will remain untapped.
The right data drives the right credit risk models
The global risk analytics market size is projected to reach USD36,670mil by 2028, from USD 20,230mil in 2021, representing a CAGR of 8.4%—largely driven by growing digitization, business process automation, and reactions to increasing data and security breaches.
Process automation is a critical part of this landscape, as enterprises seek to find real-time solutions with ongoing credit risk monitoring that enables rapid decision making that reduces risk. If the data that informs those decisions is bad, incompatible, or even just snarled in inefficient data systems, then the value of those decisions is quickly undermined. That’s why ensuring you have the right data system in place is key for good credit risk management.
According to research by McKinsey, banks which have adopted data-driven, next-generation credit models have seen a multitude of benefits. That includes between 5% and 15% improvement in revenue thanks to higher acceptance rates, lower cost of acquisition, better customer experience, reduction in credit-loss rates of up to 40%, and efficiency gains of between 20% and 40%.
The McKinsey report notes “Based on those three benefits of improved credit-decisioning models, the average bank with €50 billion in assets from small and medium-size enterprises (SMEs) could see €100 million to €200 million of additional profit.” Those are encouraging numbers which show the value generation for enterprises seeking to embrace this opportunity, regardless of their industry of operation.
Of course data integration is not without its challenges. Hurdles include barriers such as technical capabilities, technology, cultural hurdles, limited data sources, and inflexible models that have been gradually iterated over time. These must be addressed if enterprises are to tap into the widespread benefits.
Delivering an effective solution in telecommunications
Telecommunications providers have a lucrative opportunity to embed next-generation credit risk management into their own operations, leveraging the huge wealth of data generated in this connected industry.
While they may face some of the same barriers seen in financial institutions, telecommunications operators enjoy a favorable position that could allow them to sidestep some of the challenges seen in the more regulated and risk averse financial services industry.
Telecommunications is an industry built on data, so the right foundations are already in place. It is less culturally entrenched, and more open to innovation, providing a path to strong company buy-in and executive support. That same innovative approach reflects an industry more prepared to adopt new technologies and technical transformations which form the lynchpin of these efforts.
At Neural Technologies, we have decades of experience working with communication service providers (CSPs), and we see that encouraging attitude to evolution apparent in customers around the world. This is an industry passionate about improving efficiency and optimizing processes for both business and customer benefits.
Neural Technologies’ Credit Risk Management solution has helped deliver on this opportunity for CSPs around the world. We know from experience that the right data management is the make-or-break of this opportunity. If the data in is poor, credit risk teams could spend as long chasing and trying to reconcile data as they may previously have done manually checking credit risk models.
Neural Technologies has designed a solution which aims to address those common pitfalls. It allows configurable data integration that aggregates data from multiple sources, meaning any data format and type from both new and existing legacy data systems. It incorporates AI/ML solutions to deliver automated credit risk analysis capable of assessing high-volume and complex real-time transactional data, with predictive analytics which can help identify and address risks before they impact enterprise finances.
This customizable end-to-end solution offers a next-generation future for credit risk management. It leverages rules, statistical models, and machine learning to deliver an effective solution which can adapt to the dynamic credit landscape, with behavioral profiling that offers an instant view on customer spending behavior, credit limits, exposures, and tolerance over time.
That integrated approach offers a pathway to a better credit risk future for CSPs. It means a solution which can adapt to your unique business needs, while scaling and evolving to reflect a changing business environment. That’s what next-generation credit risk management should mean, and that’s how it can unlock the greatest value for your business.