Artificial intelligence (AI), notwithstanding its long-standing existence, has seen a significant surge in business applications over the past few years – some good and some bad. This mixture of positive and negative impacts is nowhere as evident as in credit and risk management where AI can both be the source of problems and the solution.

“AI can have a massive impact on speeding up development lead times and enhancing security, but it’s crucial to keep the human element in the loop to make the best possible decisions,” says Frank Knight, CEO of Debtsource.

He emphasises the necessity of keeping human interaction at the core of their business, even as they integrate AI. “The moment we move this business away from human interaction into an AI-dominated one, we risk losing the essence of what we do.”

Knight says there are some exciting developments on the horizon in credit management, including a new credit application process integrated with biometric verification. This innovation aims to eliminate fraud without compromising the speed or convenience of the application process.

“AI enables financial institutions to manage and mitigate risks associated with credit decisions effectively. By analysing historical data, market trends, and other critical factors, AI-powered risk management systems provide data-driven insights that minimise risks, optimise credit decisions, and reduce potential financial losses.”

On the other hand, AI’s ability to simulate fraudulent processes poses a significant concern, particularly in processing credit applications.

AI-powered analytics and predictive modelling enhance the efficiency and scalability of financial services, ensuring quicker and more accurate outcomes. This is particularly beneficial in credit decision processes, where AI can automate complex historical data analysis.

AI’s capacity to analyse large datasets from multiple sources in real-time is crucial in identifying and mitigating fraudulent activities. By detecting patterns and anomalies indicative of fraud, AI helps financial institutions protect their clients from potential threats and to maintain trust. For instance, AI-powered document automation fosters digital collaboration, improves accuracy, and reduces manual errors and processing times.

Knight recommends organisations wishing to implement AI in credit management to ensure all shared data is correctly classified and stored appropriately.

“Implement zero-trust policies for data access, granting users the permissions only necessary for their roles. Then establish a comprehensive AI policy outlining guidelines and controls for the responsible and ethical use of AI within the organisation.”

He notes that the flipside is that as AI adoption grows, so do the associated cybersecurity threats. He therefore recommends businesses stay updated with the latest trends in cybersecurity and threat protection as a safeguard against malicious actors.

He emphasises the need for customised AI solutions to handle critical and intensive data, perhaps requiring professional IT providers for tailored AI implementations.

“The effectiveness of any AI model hinges on the quality of the data it processes. Ensuring clean, accurate data is essential before embarking on AI initiatives. For instance, our data is encrypted and protected by extensive firewalls, employing end-to-end encryption to maintain data integrity and security. While no system is entirely hack-proof, effective IT policies and processes are vital in minimising risks.”

While concerns are commonly raised about AI potentially replacing human expertise in cybersecurity, the likelihood is that AI would not replace human cybersecurity professionals but rather augment their capabilities. “AI can enhance fraud detection, respond to fraudulent activities and streamline security processes, allowing humans to focus on more complex and creative tasks. AI supports the efficiency of tasks but does not replace the nuanced judgment required in critical decision-making, particularly in commercial credit scenarios where high-value decisions are involved.”

Ensuring data integrity is paramount – with millions of data pieces, it is crucial to maintain a single version of the truth by integrating diverse information sources, including aging data, trade references, adverse information, and operational data.

Adherence to regulations is critical. For instance, Debtsource operates under the scrutiny of multiple regulators, ensuring that all AI applications comply with legal standards.

Knight emphasises the importance of managing data with common sense and integrity, beyond mere regulatory compliance.