AI-powered use cases poised to take financial services to the next level

In recent years, the business world has increasingly turned to smart solutions to deal with the changing digital landscape. Artificial intelligence (AI) enables devices and objects to perceive, reason, and act intuitively, mimicking the human brain, unhindered by human subjectivity, ego, and routine interruptions. Technology has the potential to significantly expand our capabilities, bringing increased speed, efficiency and precision to both complex and mundane tasks.

To get a sense of the momentum behind AI, the global artificial intelligence market was valued at $ 62.35 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 40.2% from 2021 to 2028. Given this projection, it’s no surprise that tech giants like AWS, IBM, Google, and Qualcomm have all made significant investments in research, development, testing. disparate impact and auditing.

My area of ​​expertise, fintech (financial technology), is no exception to this trend. The AI ​​fintech market alone is valued at around $ 8 billion and is expected to grow to over $ 27 billion over the next five years. AI and machine learning (ML) have penetrated almost every facet of the space, from customer-centric functions to back-end processes. Let’s take a closer look at these changing dynamics.

What use cases of AI and ML are applicable to fintech?

Financial services have their own set of common AI and ML use cases. These include, but are not limited to, reducing costs, automating processes, reconciling expenses, analyzing data and improving the customer experience. According to a Bath report, the pandemic has widened the gap between top-performing companies and other companies in three main drivers of productivity: time, talent and people’s energy. These AI use cases should help address the business challenges associated with this gap.

Process automation, one of the most common use cases in the industry, reduces the amount of manual work done by employees by automating repetitive tasks and processes. Not only does this reduce boredom, but it also gives workers time and energy to focus on innovation and other value-added activities, which can also boost morale.

The expense reconciliation and payment authorization processes are also very labor intensive and time consuming for accounting departments. AI and ML can provide automated three-way matching of incoming supplier invoices for approval. Intelligent systems can also learn the approval process for more complex and fragmented areas of expenditure, such as the various travel, goods and services expenses of today’s increasingly dispersed workforce. hui.

For example, Beanworks, an Accounts Payable (AP) automation provider, recently introduced SmartCapture, an AI-powered data capture feature that promises to dramatically increase the speed and accuracy of customer data entry. The company claims its offering delivers over 99% accuracy while completing AP processes in minutes. Combined with its SmartCoding technology, it claims to reduce the time that accounting teams spend entering data by more than 80%.

According to Beanworks, traditional manual entry of AP data is inefficient, error-prone, and consumes considerable time and resources, costing between $ 12 and $ 20 per invoice to process. An AI solution like SmartCapture, according to Beanworks President and COO Karim Ben-Jaafar, gets smarter with every invoice as it learns how to correctly interpret an organization’s accounts payable documents and coding. This approach, said Ben-Jaafar, “frees up the time and energy for accountants to focus on other more strategic tasks.”

Other AI and ML applications help consumers better manage their finances with hyper-tailored offers tailored to their current financial situation, including credit on demand and at lower cost.

In a recent column on the impact of the pandemic on credit trends, I quickly touched on Reached, an AI lending platform that reinvents creditworthiness. It’s worth taking a closer look at the company’s efforts to overhaul our outdated credit rating system, which as it stands makes it difficult for lenders to accurately assess which borrowers are likely to fail. This uncertainty often results in consumers being denied access or overcharged for credit. Meanwhile, Upstart’s system relies on AI to instantly approve more than two-thirds of its loans.

AI credit scoring software can reduce non-performing loans while increasing yields, which means better lending decision-making. This means businesses lend to less risky clients, and clients can access personalized, near-instant lending decision-making.

In keeping with the theme of on-demand finance, two of the most sought-after AI / ML solutions are personalized portfolio management and product recommendations. Their popularity is growing, as is their sophistication. Investment platforms such as Improvement recommend investment opportunities for clients based on income, current investing habits, risk appetite and more. The wine investment platform, Vinovest, uses custom machine learning algorithms developed with world-class sommeliers to organize a portfolio of wines that acts both as an alternative investment that can outperform other asset classes and as a “liquid asset” if the user actually decides to drink his wine.

In the years to come, we will likely see a refinement in all of these technologies. I think we’ll also see increasing automation in customer support, reporting, and data analysis.

Can we trust “that”?

As financial service providers continue their digital transformation, we will see an increase in AI and ML security solutions over the next year. For example, I expect to see more regulatory solutions (RegTech) that scan documents for account registration, detect anomalies in patterns within accounts, and more. AI and ML have gone from curiosity to necessity and priority during the pandemic, especially for financial services.

AI needs an audit: we can’t assume that what the machine learns will always be correct. As long as the proper audits are in place, AI can bring security to financial services. Certainly, any technology used in modern banking requires a strong regulatory focus. Creators of AI in financial services should prioritize the audit trail when building their solutions.

Gene Ludwig, founder and former CEO of Promontory Financial Group, an IBM-owned financial services consultancy, summed this up well in a podcast on the Reinventing Financial Services digital forum. “AI, properly documented, is actually better than the human brain because you can’t get inside the human brain and document the decision model used. It’s actually potentially much better, not worse than people in terms of audit trails.

A gratifying result

What we’ve learned from the rise of fintech apps is that customers are looking for instant gratification, whether it’s in terms of response time or personalization of their experience. The sophistication of AI and ML will give space leaders those precious minutes, if not seconds, it takes to compete for someone’s business while delivering a personalized product to them. From customer-centric functions to back-end processes, the possibilities are endless.

Disclosure: My firm, Moor Insights & Strategy, like all research and analysis firms, provides or has provided research, analysis, advice and / or advice to many high-tech companies and consortia in the industry. I do not hold any stake in the companies or organizations mentioned in this column.