At the recently concluded 62nd ELFA Annual Convention, various sessions and casual conversations around AI unfolded a rich tapestry of possibilities -- ways in which companies can leverage AI to transform every aspect of an asset finance and leasing business.
It was during a casual conversation that a fellow attendee shared an intriguing perspective on AI, saying, "AI is going to augment everything that we do -- every day. From that perspective, it should be called augmented and not artificial intelligence."
I’m not sure if it was an original thought or if my fellow attendee was quoting someone else -- but the thought stuck with me.
Indeed, AI has the potential to revolutionize how asset finance companies operate. The question remains: if it’s so beneficial, why are we not witnessing an explosion of AI-enabled use cases in the industry?
To AI or not to AI
The buzz at the event, from keynotes to discussions, centered around AI. Yet, there's a noticeable gap between recognizing AI's impact and having clear strategies for successful implementation within organizations.
A significant challenge in embracing AI is identifying high-impact use cases with promising returns. Companies in asset finance grapple with determining areas of maximum business impact, ROI, technology readiness, risk reduction, and minimizing disruption to daily operations. According to McKinsey’s The State of AI in 2023 report, most companies identify defining the right strategy and finding the right models and tools as the biggest challenges to their AI adoption (see figure below).
Models and tools pose the biggest AI-related challenge for high performers, while strategy is a common stumbling block for others.
Note: Figures do not sum to 100%, because of rounding.
1Asked only of respodents whose organizations have adopted AI in atleast 1 function.
2Respodents who said that at least 20 percent of their organization's EBIT in 2022 was the attributable to their use of AI. For respondents at AI High performers, n=49, for all other respondents, n=792.
References: 1. Finextra - Generative AI: The Missing Piece in Financial Services Industry.
Early projects from various financial institutions provide insights, pinpointing specific areas where AI has already demonstrated a significant impact. These use cases offer calculated risks and are supported by existing technological solutions that have delivered proven results for other financial services organizations. They can help you build a strong and convincing business case for your AI initiative and gain C-suite approval. Mastering these areas sets the stage for pioneering innovative AI applications within organizations
Automation of routine tasks
- Automation of routine tasks: What's remarkable is that automation, often rumored to eliminate jobs, is now eliminating mundane, routine work, so that employees can add more value to the business. From document verification to credit assessments and data entry, intelligent automation streamlines daily business operations, reducing human intervention and minimizing errors. This not only accelerates processes but also ensures greater accuracy, creating a win-win for all stakeholders.
Predictive analytics for risk assessment
- Predictive analytics for risk assessment: AI is transforming traditional, time-intensive processes.
Fraud detection and prevention
Fraud detection and prevention: AI algorithms detect anomalies in financial transactions, identifying potentially fraudulent activities and mitigating risks. Continuous monitoring of financial transactions allows AI systems to pinpoint suspicious behavior and trigger alerts, enabling companies to take immediate action to prevent financial losses.
Delivering exceptional customer service
- Delivering exceptional customer service: AI is revolutionizing the provisioning of customized offerings through AI-driven analytics that delve into vast sets of customer data and behavior patterns.
- Asset maintenance: Predictions are crucial for the asset finance and equipment leasing industry. AI can predict maintenance needs by analyzing usage patterns and sensor data. This proactive approach minimizes downtime and operational costs, ensuring that the equipment stays in optimal condition. The ability to predict maintenance prevents unexpected disruptions and contributes to cost savings.
Real-time portfolio management
- Real-time portfolio management: For companies managing a diversified portfolio of leased assets, real-time monitoring is crucial. AI can assist in monitoring and analyzing network traffic for these assets, ensuring optimal performance, minimizing downtime, and reducing costs associated with equipment breakdowns. The advantages of real-time portfolio management extend beyond cost savings, allowing for improvements to the operational efficiency of asset finance companies.
- Preventing cyberattacks: AI plays a crucial role in enhancing security by analyzing and determining normal data patterns and trends and alerting companies to discrepancies or unusual activity. This helps protect customer data and assets from cyber threats.
Striking the balance
Embracing AI for business transformation requires a delicate balance of ambition and caution. Understanding the fundamental questions driving your business goals is paramount. Whether it's expanding portfolios, mitigating risks, seizing revenue opportunities, or cutting operational costs, clarity on priorities is key. Start with a focused use case, allowing room for learning and growth. As AI technology evolves, businesses must adapt, continuously reassessing their strategies. With a practical and purposeful approach, organizations can harness the true potential of AI to enhance their operations and drive sustainable success.