How Data Quality Is Powering the Future of Intelligent B2B Payments

While the back offices of many large enterprises are hindered by technologies from the 1990s, others are embracing new digital solutions.

Despite institutional inertia, innovation remains non-negotiable in the evolving landscape of B2B payments technology.

The latest frontier? Agentic artificial intelligence (AI), an evolution of enterprise AI.

Unlike its predecessor, GenAI, which focused primarily on producing content, agentic AI takes automation a step further by incorporating decision-making capabilities.

“An agentic AI model could actually be looking at the transactional data, proactively identifying trends, checking for data anomalies, reaching out to stakeholders, triggering the right workflows and making sure everything stays on track,” Rinku Sharma, chief technology officer at Boost Payment Solutions, told PYMNTS during a discussion for the most recent edition of the What’s Next in Payments Series, “The Rise of Digital Labor: Exploring Agentic AI in Banking and FinTech.” 

“It’s about intelligently acting on the data, taking decisions and creating those automated workflows that create the scale that is needed,” Sharma said, noting that the technology’s capacity to act autonomously is what sets it apart from standard automation models.

From loan underwriting to fraud detection and financial advisory services, agentic AI is poised to potentially revolutionize how banks and FinTechs operate.

Sharma, a certified AWS Solutions Architect and AI proponent with over 23 years of experience, is spearheading Boost’s integration of AI-driven solutions across the enterprise. 

The company’s journey with AI began with traditional rule-based automation but has now evolved to embrace the capabilities of large language models and agentic AI. 

“We are in very early stages so far, like everybody else,” Sharma said. “However, we’ve taken some giant strides in terms of embedding AI into our day-to-day operational procedures and whatever we do in terms of parsing the information that’s coming from our payments.”

Boost, a FinTech acquirer exclusively focused on the B2B marketplace, processes payment information from numerous platforms daily, with that volume increasing as their network of issuers, buyers and suppliers grows.

Scaling this system requires more than just incremental improvements. It demands intelligent automation.

One key use case for Boost is reconciliation reporting. Sharma described how GenAI can create reconciliation reports based on settlement data. But with agentic AI, the system can go further, proactively detecting anomalies, alerting stakeholders and initiating workflows to resolve issues.

Through agentic AI, Boost is further automating data validation, triggering workflows and even ensuring compliance criteria like KYB (Know Your Business) are met. Sharma believes this will free operational staff to focus on higher-value work rather than repetitive, time-sensitive tasks.

Boost is also exploring agentic AI for merchant onboarding and dynamic price optimization, two critical areas where streamlined processes can yield substantial benefits.

“Merchant onboarding has historically been one of the more heavy, manual touchpoints,” Sharma said. “It requires a lot of data gathering, setting up merchants and following up for additional information.”

AI can help to change all that. And as it relates to dynamic price optimization, Boost’s existing rules-based systems are evolving through AI to make real-time decisions based on transaction cost, currency, customer preferences and other variables.

“We’re looking to leverage agentic AI to enhance that further and make those decisions in real time,” Sharma said. “It’s about finding the best route for a transaction and continuously improving the process.”

Still, agentic AI implementation is only as good as the data it receives. Sharma stressed the importance of data validation and readiness as foundational pillars of Boost’s AI strategy.

“The models are only as good as the data being fed to them,” he said. “Garbage in, garbage out holds true even with agentic AI.”

For its own part, Boost ensures quality data by implementing several layers of validation, enrichment and standardization before feeding it into their AI systems. Once the AI produces results, these are verified against internal data sources for accuracy.

For Sharma, the focus remains on pairing innovation with accountability.

“We want to make sure that these models are not just smart, but they’re accountable as well,” he said. “Validating the model outputs is what we believe in.”

As Boost scales its operations and continues to adopt new platforms and clients, Sharma remains committed to maintaining a balanced approach to AI integration.

“We need automation, but not just automation. We need intelligent automation that can take decisions and help us move faster,” he said. “In the next few years, agentic AI is going to be a foundational part of the B2B payments industry. Here at Boost, we want to be the industry leader.”

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