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How to Transform Credit Risk Decisioning Challenges into Opportunities Ensuring More Accurate, Agile Decisions
Common Challenges Financial Services Providers Face in Credit Decisioning:
Data Quality + Accuracy: Difficult to access and integrate real- time data into credit decisioning processes, for a more holistic view and accurate risk assessments.
Integration + Compatibility: Inability to seamlessly integrate automated decisioning technology into existing systems and processes.
Scalability + Flexibility: Lack of agility in launching new products quickly and seamlessly and inability to adapt to changing market conditions and evolving customer demands.
Regulatory Compliance + Security: Challenging to comply with industry regulations and data security standards efficiently and effectively.
Detecting + Preventing Fraud: Sophisticated fraudsters and their rapidly evolving methods are difficult to keep up with.
Model Development + Validation: Trouble creating, testing, and iterating predictive credit risk models for accurate decisioning.
Challenges î‚« Opportunities:
Invest in Data: Improve quality and reliability of your data by partnering with reliable data providers who can provide real-time, on-demand data access to a variety of data sources (including alternative data). Learn More
74%
of decision-makers struggle with their organization’s credit risk strategy because data is not easily accessible ( Pulse Survey)
Leverage API Solutions: Use API-based solutions (like data!) to ensure seamless integration and compatibility with your existing systems. Learn More
50% (TitleMax Case Study)
Streamlined data integration can cut loan approval time by
Use Advanced Analytics: Look for embedded machine learning capabilities to better predict patterns and make more accurate risk decisions. Learn More
Banks that have embedded high-performance credit- decisioning models into digital lending have seen revenue increases of 5-15% with higher acceptance rates, lower cost of acquisition, better customer experience ( McKinsey)
Fight Fraud with Data: Ensure a holistic, real-time view of data, which integrates easily into decisioning processes to stay on top of evolving fraud threats. Learn More 20-40% improved efficiency with more automated data integration and risk decisioning, and advanced model development ( McKinsey)
Banks see decrease of 20-40% in credit loses using more precise risk assessment models ( McKinsey)
Ease Compliance with the Cloud: Adopt cloud-based platforms that can evolve with changing regulatory and compliance needs, while ensuring reliable performance and data security. Learn More
Low-Code for High Agility:
Be self-sufficient with low- code decisioning solutions that allow you to create, test, and iterate your workflows independently, ensuring maximum agility and flexibility to keep up with your
competition. Learn More
With Automated, Upgraded Credit Decisioning You Can: Focus on delivering a frictionless customer experience without sacrificing your risk strategy Regularly assess the performance of your credit models and make changes as required, quickly and easily Collaborate easily between departments and teams, eliminating silos and enabling a cohesive risk decisioning strategy
Enable faster, more accurate credit risk assessments
Launch new products and regions faster than your competition
Looking to upgrade your credit risk decisioning technology?
Discover How
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