Depending on who you ask, artificial intelligence (AI) and machine learning (ML) are at different stages of maturity in the finance industry, but there is widespread agreement that the technologies are trending upward.
On a global scale, AI is expected to become a major business driver across the financial services industry, according to the World Economic Forum (WEF). Seventy-seven percent of finance executives anticipate AI "to possess high or very high overall importance to their businesses within two years," according to the findings of a WEF survey released in January.
Specifically, AI will be incorporated into generating new revenue potentially through new products and processes; process automation; risk management; customer service; and client acquisition within the next two years, according to 64% of the WEF survey respondents. The survey included responses from 151 financial institutions in more than 30 countries.
For now, though, AI and ML remain in its infancy. The deployment of AI is more nascent in financial organizations, limited to 2% of financial planning and analysis organizations, according to Gartner's 2019 report, Digital Technology Adoption Trends in Finance.
Before implementation, the analyst firm recommends that finance leaders focus on developing a strategy, business case, and workforce development.
The promise of AI is that it "allows for precise revenue forecasting using a larger number of data inputs," the Gartner report said. In accounting, for example, "AI with machine learning capabilities can flag costly accounts."
This sentiment is shared by BillingPlatform, a provider of enterprise billing solutions, which has started incorporating AI into its platform.
In the finance function, the idea is that machine learning and AI can go through tremendous amounts of data to automate tasks more easily, and make more accurate predictions than a human can, said Nathan Shinn, co-founder and chief strategy officer, BillingPlatform.
This can help a user see what their revenue will look like next year based on trends from last year, Shinn said, adding that this will be easier for some organizations to predict than others -- a business with flat-rate subscriptions as opposed to one with variable revenues, for example.
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The technology, which gives a system the ability to come to conclusions on its own based on outcomes, can detect things like customer churn based on criteria fed into a machine learning algorithm, Shinn said.
"So we can see this person has skipped a few payments, or in credit card processing, if we got a failed transaction, machine learning can help in not just retrying the credit card X number of times; it can look back and see when we were more successful [in processing the payment]," Shinn said. This can be done by looking at past payment patterns.
Fraud detection is another area where AI is expected to make a big splash, Shinn said.
However, "very little of this is happening right now,'' he said. "The evolution of machine learning is the machine's ability to teach itself stuff based on outcomes."
Right now, systems engineers are still needed in the middle, "so it's very rare to see real machine learning ... but it will grow like anything else."
There is a "high mismatch" between the Gartner data on the low number of chief accounting officers who said they are deploying AI right now compared to what CEOs are doing, observed Alejandra Lozada, a senior research director at Gartner. "About half said AI will be the most important tech trend in the coming years," she said. Gartner predicts that more than half of finance organizations will employ some form of AI by the end of 2021, Lozada said.
Yet, others are already seeing greater adoption of AI in finance. According to Deloitte's 2nd State of AI in the Enterprise study, financial services and insurance are among the industries seeing high returns on AI investments.
"This makes sense, given the nature of how financial institutions operate and compete in the market,'' said Beena Ammanath, AI managing director, at Deloitte Consulting LLP. "At the highest level, by utilizing machine learning, financial services organizations can pinpoint their most effective offerings, change how they attract and keep customers by identifying patterns in their behaviors/interactions with the institution, and [create] opportunities for engagement beyond just financial services."
Machine learning is being used to extract knowledge from observations and to automate judgement-based processes, added Gartner's Lozada. For example, Gartner is working with large companies that are using machine learning to predict the probability of clients making payments on time.
One Gartner client has developed a model that predicts the high probability of certain customers paying late, so the client has started reaching out proactively on day 10 instead of waiting until day 30 to remind them to pay, she said. "This is different because most companies reach out after they are late, and this company reached out in advance'' to those at risk, she said. "This is a predictive way of managing collections."
The average turnaround time to settle invoices was reduced by 40%, Lozada said.
But this is a cutting-edge example. Currently, fear of the unknown is holding back a lot of finance organizations in their AI deployments, said Lozada. This is because of unfamiliarity with what AI can do, uncertainty about where to start, and not having a strategy in place, she said.
Other issues include not having enough information about various vendors' offerings and confusion about what to select and whether a product can be integrated into existing systems, and if so, how, she said.
"Obviously, another issue is enterprise maturity -- not having the right governance processes and skill levels in adopting AI." This is not just specific to finance departments, but applies across the board, Lozada said.
It's important to come up with a starting point, a strategy, and ensure that you have the skills to implement AI in day-to-day functions, she said.
Both Lozada and Ammanath agree that as AI moves into finance operations, organizations must ensure safeguards are put in place.
"There are many inherent risks that come along with AI and machine learning such asdata bias, insufficient data protection mechanisms, lack of experienced AI talent, lack of training for responsible parties, etcetera, that can lead to unfavorable outcomes,'' Ammanath said. "Used unethically, even inadvertently, AI can result in significant revenue loss, stiff fines, and a more intangible and priceless asset: an organization's reputation and trust of its customers and internal stakeholders."
Lozada stressed that as they proceed with AI and machine learning initiatives, organizations must ensure the data is transparent, explainable, and ethical before doing predictive analytics in the future.