Moody's cautions businesses in its most recent in-depth research that if they want to successfully incorporate artificial intelligence (AI) into their operations, they must have a well-thought-out implementation strategy.

This also holds true for financial services firms, as using AI at a rapid rate requires careful balance. While progressing too slowly might jeopardize a FI's competitive edge, integrating AI too rapidly could raise the probability of incorrect results.

Errors may be expensive, both in terms of money and reputation. This was evident with Alphabet Inc.'s Gemini Assistant, which at one point produced pictures that were not correct.

AI models: Juggling the ideal with the real-world

Moody's advises businesses to first create AI models that strike a balance between idealistic goals and practical considerations in order to lessen the negative impacts.

Naturally, there are several challenges in creating a risk-free AI model since numerous requirements must be met at once.

The requirements for a risk-free AI model are outlined by MOODY's.

• Sturdy Performance: The model has to produce correct findings on a regular basis.

• Fair and ethical: The model ought to reflect human values.

• Information about AI systems should be accessible to users and easily interpreted.

• Law and regulation compliance: The infrastructure and model need to be in compliance

• Secure and confidential: Strict cybersecurity protocols need to be put in place.

• Energy-efficient: The model ought to use the least amount of energy.

• Resilient: The model has to be able to adapt quickly to changes in data quantities and use.

• Low upkeep: AI applications should be easy to use and require little maintenance.

• Positive financial impact: The advantages of the model must exceed its drawbacks.

Naturally, many of these needs are compatible with one another, and Moody's states that in order to balance these elements as advantageously as possible, FIs must make trade-offs.

Low-latency outcomes are costly, unbiased output might impair performance, and strong AI models need a lot of energy to operate. When thinking about their AI rollouts, organizations need to weigh these factors against other indicators. 

Since most people cannot create perfect, risk-free AI models, financial services companies must be clear about the risks they are ready to take in order to meet their strategic goals.

FACTORS THAT WILL AFFECT RISK TOLERANCE ARE OUTLINED BY MOODY'S:

• Business-to-consumer or business-to-business: B2C carries a larger risk.

• Reputation: If an AI model fails, trustworthy financial services risk losing their trust.

• Industry: If AI fails, sensitive industries might suffer severe repercussions.

• Function of AI model: What data would a business give an AI model access to?

• Model complexity: AI models with fewer moving elements are simpler.

• Internal development: The training expenses of third-party AI models are high.

• Laws and regulations: AI in banking is subject to a number of intricate restrictions.

Developing an AI strategy: Prevent lowering the quality of credit

Artificial Intelligence is such a disruptive technology that Moody's warns organizations that it has the potential to completely change the business models of the loan issuers that it ratings.

AI will have an impact on investments, productivity, and product offers. Organizations risk having their credit quality decline if they are unable to handle this technology change.

If organizations fall behind by adopting too cautious AI adoption strategies, their competitive standing will worsen and they will lose their competitive advantage.

There are a few possible outcomes for this, the most apparent of which is that businesses completely avoid implementing AI. The infrastructure and regulatory compliance challenges that many legacy banks have might slow down the adoption of AI, but if these issues are not overcome, financial institutions (FIs) risk being at a competitive disadvantage.

Furthermore, pre-made AI programs could not provide the amount of uniqueness required by financial services firms. Since AI chatbots, for instance, are already widely used, it would be preferable to deploy unique, customized Gen AI services, even though doing so is more expensive and complicated.

OTHER OBSTACLES TO THE ADOPTION OF AI INCLUDE:

• Businesses could find it difficult to use AI at scale.

• AI models' performance might not be up to par.

• There might be harm to reputations

• Users might not comprehend how an AI model arrived at a forecast.

Applications of AI provide unique difficulties.

Finally, Moody's notes that while though AI applications today have a lot of promise, many nevertheless have their own set of difficulties.

Applications of AI fail silently when they do. It's true that some businesses might not realize their AI solution isn't functioning as planned until it's featured in the media. In one instance, Air Canada had to give a client a refund after its chatbot misinformedly claimed they were eligible for reimbursement.

The graph that follows identifies the main causes of AI system failure.

Following the publication of its in-depth analysis of the five indicators of financial crime at shell corporations and its AI prognosis for 2024, which examined the effects of AI on financial services firms this year, came the release of Moody's most recent research.