By David Schmidt and Robert Shultz
By implementing process automation in conjunction with today’s AI-driven tools, you can enhance your company’s ability to sustain operations and grow, in many cases exponentially. AI-driven insights and real-time monitoring are transformative tools that enhance functions such as credit decision-making efficiency and consistency while maintaining compliance with regulatory requirements.
Successful implementation of these third-party tools, however, requires careful evaluation. Avoidance of pitfalls is critical. Finance leaders must consider the provider’s capabilities, data quality, and ability to provide ongoing model improvements.
When choosing an AI solution for any back-office function, CFOs and other financial leaders must determine the features and capabilities the company needs. Each organization will differ in terms of the complexity of its infrastructure, IT, administrative resources, industries served, sales channels, geographic scope, transactional size, and volumes.
If several critical factors are not carefully considered and analyzed, the costs related to business disruption and risk of failure may outweigh the potential benefits.
12 Features to Consider When Implementing Technology
Twelve Features to Look for When Implementing Technology:
1. Robust Data Integration and Processing:
The AI solution should integrate seamlessly with existing systems (e.g., ERP, CRM) and consolidate data from various sources like financial statements, bureau reports, credit reviews, and market data. Real-time integration of accounts receivable, disputed balances, cash receipts, and referral statuses ensures comprehensive credit evaluations and monitoring.
2. Advanced Risk Assessment Models:
Look for AI models that assess creditworthiness by analyzing multiple dimensions. For example, historical payment behaviors, macroeconomic factors, external rating updates and sector-specific risks. Algorithms should be capable of weighing various risk indicators to provide a nuanced credit score for each customer.
3. Automated Decision-Making with Human Oversight:
The AI solution should automate decision-making with options for human intervention in complex cases. Credit professionals can assess decisions based on financial, sales, marketing goals, and risk tolerance. This blend of automation and oversight ensures efficiency while maintaining accuracy in high-risk or unusual cases.
4. Real-Time Credit Monitoring & Alerts:
Continuous credit exposure monitoring is crucial. The solution should trigger real-time alerts for signs of deteriorating creditworthiness, such as overdue payments, credit limit breaches, score changes, or economic shifts. Early warnings enable proactive risk management and timely adjustments.
5. Adaptability and Scalability:
A good AI solution should be scalable to manage increasing transaction volumes and adaptable to the company’s evolving needs. It should also accommodate new data sources and risk factors as the business grows, making it a future-proof investment.
6. Customization and Risk Scoring Flexibility:
Every company will have unique risk tolerance and be subject to changing conditions. This accentuates the need for customizable risk scoring models. The solution should allow companies to adjust credit parameters, such as score thresholds and risk tolerances, according to their specific industry, market, and customer base.
7. Predictive Analytics and Scenario Analysis:
Predictive analytics can help creditors anticipate customers’ future financial performance based on historical data and current trends. Scenario analysis tools allow for stress-testing of credit portfolios against economic scenarios, which enhances preparedness and risk planning.
8. Compliance and Security Features:
The solution should support compliance with industry regulations (such as the FCRA, CECL, etc.) and data protection standards. Strong security protocols for data privacy and protection are also essential to safeguard sensitive financial information.
9. Transparency of Decision Dynamics:
AI models should offer explainable outputs that enable creditors to understand the factors driving each credit decision. This transparency is important for regulatory compliance and for building trust with internal stakeholders, such as Sales, as well as with customers, by providing clarity on credit terms.
10. User-Friendly Interface and Reporting Tools:
A user-friendly interface with intuitive dashboards and reporting tools makes it easy for credit teams to monitor exposure, review customer portfolios, and track KPIs. The solutions should also have a robust capability to generate dashboards, and reports for internal stakeholders, senior management, and auditors.
11. Integration with Payment and Collection Systems:
Integration with payment and collection systems is essential for end-to-end credit management. This feature helps to prevent overdue accounts from escalating and enables automatic adjustments to credit limits and terms based on payment behavior and collections history.
12. Continuous Improvement and Support:
A solution that adapts to changing market conditions and incorporates new data for improved accuracy is essential for long-term effectiveness. Choose an AI solution that will provide regular updates, training, and ongoing support.
