Rethinking How AI Can Address Shifting CFO Concerns

(This article written by Scott Nelson & Tim Appleget originally appeared in the March / April 2023 issue of Newsline Magazine )

The news headline for the recent release of the 2022-2023 US Bank CFO Insights Report declared that “Risk Management Soars Past Revenue Growth as Top Priority for Finance Leaders” – highlighting a major shift in the concerns of financial leaders from financial to operational risk. That shift opens the door to new approaches to using data and digital technology, specifically AI, to navigate the choppy waters created by shifting economic trends – high and higher interest rates.

Top concerns for CFOs shifted from financial management – revenue, costs and cash flows – to operational risk management – new/unknown risks, talent and economic shifts.

Given that enterprise-wide adoption of digital technology had equal concern year-over-year, one could conclude that CFOs would be early adopters of AI because the technology is uniquely positioned to help with both last year’s and this year’s challenges.

AI is certainly recognized as a leading-edge technology play in any business today and is often, perhaps most often with financial leaders, associated with workflow automation that reduces labor costs. But, as will be shown below, the technology is also well suited to help CFOs with operational concerns like those highlighted in this year’s survey, including addressing talent shortages.

So why should CFOs support an investment in AI?

40% of finance leaders identified talent shortages as a top business risk.

Perhaps the biggest swing from the 2021-2022 to the 2022-2023 survey was from planning to implement cost-cutting layoffs to talent acquisition as the top business risk. Talent shortages are frequently in the news and most often described in operational occupations like manufacturing, food service, construction and trucking. But the reality of today’s demographics is that talent is going to be a precious resource for almost all industries and occupational roles for the foreseeable future. U.S. fertility rates have been below population replacement levels for three decades and now low labor participation rates are further undermining staffing efforts. So, while one could say that the CFOs should have had this on their list for several years, better late than never.

Ironically, data engineers with AI experience are described as invaluable by recruiters because they are viewed as a cost-saving resource deploying AI automation to reduce labor. But there is another way to look at AI in the context of a talent shortage. AI is a learning technology, by definition, and as such can augment and help train workers faster and more effectively than traditional methods. Consider the following applications of AI in the human resource context.

Talent Pool Expansion

AI increases the hiring pool size by informing, recommending and augmenting the decision-making skills of less experienced employees. An AI agent trained on outcome data generated over the past five years by highly skilled and experienced decision makers provides predictions and recommendations to less skilled replacement workers based on those positive outcomes thereby transferring good expert-level decision making skills. As the chart below shows, a less experienced labor force can be 10 to15 percent larger and continues to grow as recruits complete school and enter the labor force.

Prime working age population in the U.S.

~10% more workers in 5 to 10-year experience range than 15 to 20

Accelerated Employee Development

AI agents can clear the simple, less challenging tasks from a workflow thereby accelerating the training of the employee by helping them focus on the problems that matter most and need experience to master. By removing routine tasks from an employee’s work flow, a more apprentice-like approach to development results as the employees see recommendations from the predictor system and collaborate with other, more tenured employees in the organization.

Worker Satisfaction and Retention

AI decision making agents remove the need for employees to perform mundane tasks making each new hire more impactful to business risk management and revenue growth, i.e., more productive. Automation of the mundane also focuses an employee’s work on more challenging problem-solving tasks improving employee engagement and satisfaction, which in turn can improve retention.

Improving risk “identification” and mitigation

The key word in the report summary regarding risk management is “identification.” Risk mitigation is a part of every business operation and those mitigations are typically implemented in work flows matching a mitigation to a risk. The challenge that seems to be unsettling CFOs this year is the risks that they don’t know, or of which they are not aware.

Data-centric cultures and AI-based predictors specifically help with unknown-unknowns in three ways:

Predictors provide probability distributions of outcomes that allow better cost-benefit analysis and quantified risk policy. A delinquent deal without loss can have a better return.

• Predictors provide probability distributions of outcomes thereby defining the risk of not achieving the outcome expected. Predictors can provide insight into the future with much more precision than a score that simply says “good or bad” without a forecast of either which enables the business to make quantified risk decisions that can improve performance.
• AI Predictors are based on machine learning models that use many more inputs than typical human-designed scorecards. The large scale of inputs can accommodate and predict outcome correlations that are atypical or that human analysis could not identify. As such, AI predictors can identify new risks associated with inputs that were previously unknown.
• Prediction outliers help identify new uncertainties or new variables of risk before they happen. When an outlier is evaluated – “Why did this deal go through when it was predicted to be a loss?” – often new judgement criteria or economic conditions are found. That new criterion, by definition, may introduce new risks to operational success.

High inflation presents opportunity for new tools used on old challenges.

Inflation recently hit a 40-year high so it’s not surprising that CFOs have it on their “risk radar.” Many business leaders today have never lived through inflation of this scale and as such are unfamiliar with the methods of managing the risks. Fortunately, the fundamentals are well known and modern equipment finance software systems provide the data needed to identify and measure these risks.

Inflation immediately hits three parts of a leasing business. New business is threatened by the one-two punch of higher asset prices and higher interest rates driving payments beyond the reach of customers’ credit capacity. Old business is at risk of increased delinquency when customers’ business wanes during recession. Finally, portfolio performance is threatened by the combination of collateral erosion and margin compression as the cost of funding rises. Again, good data analysis and AI provide mitigations to these risks.

Macro-economic changes have shifted the way financial leaders evaluate risk and prioritize the focus of their teams. AI is changing the way risks of all types are identified and managed across all parts of the business. Even the risk of finding and hiring the right people can be addressed today with faster and more effective training leveraging the data of the business in an AI-augmented, on-the-job, learning experience. CFOs have consistently understood and valued digital transformation, but as their concerns become more operational in nature, they can get relief by leveraging the better, faster decision making that application specific AI delivers.

Eric Reis taught startup CEOs to “fail fast, fail early” because it accelerated their learning and mitigated the risks of unknown unknowns in the entrepreneurial journey. Similarly, AI can sustain business performance by helping to identify risks and learn better ways to mitigate them faster.

Written by

Scott Nelson

President & Chief Digital Officer, Board Member

Scott Nelson is the president and chief digital officer of Tamarack Technology. He is an expert in technology strategy and development including AI and automation as well as an industry expert in equipment finance. Nelson leads the company’s efforts to expand its impact on the industry through innovation using new technologies and digital transformation strategies. In his dual role at Tamarack, Nelson is responsible for the company’s vision and strategic planning as well as business operations across professional services and Tamarack’s suite of AI products. He has more than 30 years of strategic technology development, deployment and design thinking experience working with both entrepreneurs and Fortune 500 companies.

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and

Tim Appleget

Director of SaaS Products

Timothy Appleget is director of SaaS products at Tamarack Technology. In his role, he is responsible for the development and implementation of Tamarack’s technology services offerings around data, IoT and workflow automation. He joined Tamarack in March 2021 after more than two decades in equipment finance with Wells Fargo. He has a deep understanding of enterprise solutions in the industry built on years of experience of leading technology and business operations teams.

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