The goal of innovation is to help businesses better tackle challenges that can feel daunting due to limited resources.
And with the news Monday (Nov. 11) that identity and compliance platform Palm has introduced an artificial intelligence (AI) filing tool, dubbed the Beneficial Ownership Information (BOI) Filing Assistant, to help speed the compliance process for small and medium-sized businesses (SMBs), using digital innovation to manage compliance is top of mind for Main Street.
After all, companies’ use of AI is one of the things that Nicole Argentieri, principal deputy assistant attorney general for the criminal division of the Department of Justice (DOJ), said in September that prosecutors will look at when assessing their compliance programs during investigations.
Compliance programs that adhere to the DOJ’s Evaluation of Corporate Compliance Programs (ECCP) guidelines are eligible for more lenient treatment when a compliance breakdown occurs.
For small businesses, AI transforms compliance from a burdensome requirement into a more manageable, strategic advantage, empowering them to meet regulatory standards without compromising on efficiency or cost in other areas.
Read more: Big Tech’s AI Tools Are Helping Democratize Growth for Small Businesses
One of the more daunting aspects of compliance is the dynamic nature of regulatory requirements. From data privacy rules like GDPR and CCPA to industry-specific regulations, the burden of keeping up can feel insurmountable for a small business owner.
Discussing banking-specific compliance, Sovos General Counsel Freda Pepper told PYMNTS that “the challenge is understanding and keeping up with the state requirements. It’s like a moving ball because the states are constantly changing their laws.”
While AI cannot eliminate all compliance challenges, it is leveling the playing field, providing small businesses with tools once reserved for larger corporations and enabling them to compete on new terms in a complex regulatory world.
By reducing costs associated with manual processing, error correction and external audits, AI-driven compliance systems allow smaller enterprises to maintain high standards without overspending. Freed from repetitive tasks, employees can focus on higher-value work, such as strengthening customer relationships or identifying new growth opportunities.
PYMNTS Intelligence’s recently-released “New Report: SMBs Race to Critical Mass on AI Usage found that 61% of SMBs surveyed report using AI to automate daily tasks.
“While we like to refer to AI as more evolutionary than revolutionary in the world of tax and compliance, there is no denying its impact and importance … using predictive analytics to anticipate changes has become an essential part of our business,” Sovos President of Revenue Alice Katwan writes in a new PYMNTS eBook, “Beyond the Horizon: How to Identify Unexpected Threats That Could Impact Your Business.”
At its core, artificial intelligence is helping small businesses by automating the heavy lifting of compliance — tasks that previously required intensive human resources or were outsourced at great expense.
Where a small business might otherwise rely on manual record-keeping or sporadic audits, AI-powered solutions provide a continuous layer of oversight. These tools analyze large datasets, spotting patterns that may indicate risks or breaches, flagging them automatically before they escalate.
AI systems can assess the risks associated with various business activities — such as client interactions, financial transactions and even vendor relationships. These assessments go beyond traditional checklists by evaluating contextual factors and historical data, helping businesses identify the areas of highest risk and respond accordingly.
PYMNTS Intelligence has found that companies relying on legacy and manual verification solutions lose above-average shares of annual sales to fraud, at 4.5%. However, firms using proactive and automated solutions, such as those powered by AI and machine learning, typically reduce their share of lost sales to 2.3%.
Compliance requirements often entail handling substantial documentation — think know your customer (KYC) and AML anti-money laundering (AML) protocols, which require sifting through client records, identification documents, and transaction histories. Here, AI’s natural language processing (NLP) capabilities come into play, allowing systems to quickly extract, classify and analyze data from mountains of documents. This process not only eliminates tedious manual work but also reduces errors and ensures information accuracy.
While compliance has long been seen as a necessary cost, AI is enabling small businesses to turn it into a strategic asset. Increasingly, in today’s shifting regulatory ecosystem, AI does the legwork, allowing companies to focus on adapting to changes, not just discovering them.
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Agentic artificial intelligence (AI) promises to improve operational efficiencies and the customer experience offered by enterprises.
The advanced technology is finding applications in loan underwriting and fraud detection, and now it’s moving across borders.
TerraPay Co-Founder and Chief Operating Officer Ram Sundaram told PYMNTS as part of the “What’s Next in Payments” series focused on exploring AI’s use in banking and by FinTechs that automated decision making and streamlined processes will continue to transform global money movement, especially as faster payments gain ground in cross-border transactions. That’s the inexorable trend, but as Sundaram put it, there’s still room, and a necessity, to have some human interaction in the mix.
In terms of global fund flows, TerraPay’s single connection ties more than 3.7 billion mobile wallets together across 200 sending and 144 receiving countries, touching 7.5 billion bank accounts. As one might imagine, coordinating and enabling the transactions is complex.
“Obviously, in the best-case scenario, everything goes smoothly, but when things are not going smoothly, that’s when the customer queries come in,” Sundaram said.
It’s no easy task to find out straight away where a transaction is, as analysts and representatives at the company have to look at logs and query partner systems.
“A lot of that work is done manually,” said Sundaram, who added that the agents “know the corridors and the markets that they are working in, but it still takes some time.”
TerraPay is using AI models with machine learning to bolster customer support and automate tasks as financial institutions (TerraPay’s client base) send payments in real time, and those payments are processed into local markets’ beneficiary banks.
“We still don’t trust [AI models] to let them respond to the customer straight away, but we can do the analysis, and then that gets reviewed by an agent who decides if [information] is accurate or not and then sends it off,” Sundaram said.
The same principles are guiding AI models and company practices to improve technical and security operations, analyzing and categorizing anomalous transactions and automating integrations with partner firms.
“Compliance is an issue where there is a lot of review needed of the alerts, and we are using [AI models] to speed up those processes,” Sundaram said.
Asked by PYMNTS about how agentic AI can be harnessed, he said: “In financial services, you can’t take chances on technology like this, which has the freedom to go wrong. You have to be careful about making sure that it’s 100% reliable before we can let things run entirely by automation.”
Agentic AI also remains pricey. For example, OpenAI is charging $20,000 a month for its specialized agents. However, Sundaram said the industry will become commoditized quickly, which will lower prices, and some open-source offerings are capable.
“There’s a fire hose of news about breakthroughs and new ideas and new ways of doing things that are coming out on a daily basis,” he said.
Data underpins it all, and Sundaram told PYMNTS that no matter what the application, the information fed into the models must be clean. Most organizations have a range of data sitting in different intra-company silos, and those silos need to come down.
In addition, the data must be structured so that it is accessible and can be synthesized by the models. Many firms may have more than 1,000 software-as-a-service (SaaS) resources to which they are subscribed but are not accurately tracked or monitored.
“Every database is separated, each one sitting somewhere else,” he said.
The days of stitching together those separate SaaS offerings to run an enterprise are ending, he said, and we’re headed to a future when data is collected in one place.
AI models and agentic AI “are extensions of what we’ve always valued at TerraPay, which means building the most efficient infrastructure possible in order to make sure that transactions are processed safely, quickly and affordably,” Sundaram told PYMNTS. “We see AI and [AI models] as powerful tools that help us scale all this very quickly while making sure we build more and more efficiency into the system.”
