Pushpendra Mehta, Executive Writer at CTMfile.com and Host of the OpenTreasury Podcast, interviewed Rinku Sharma, Chief Technology Officer (CTO) at Boost Payment Solutions, Inc.*

The interview has been lightly edited for clarity and length.

This interview focuses on the role of artificial intelligence (AI) in business-to-business (B2B) payments—specifically, how Generative AI (GenAI) is being integrated into payment automation and fraud prevention strategies; some of the biggest opportunities for GenAI in transforming core financial operations like Accounts Payable and Accounts Receivable; and more—providing valuable insights for both payments and treasury professionals.

Rinku Sharma is an accomplished senior technology leader and AI evangelist with over 23 years of experience driving transformational initiatives across diverse industries, including financial services and media. As a certified AWS Solutions Architect and Project Management Professional (PMP), Rinku specializes in designing and implementing scalable cloud architectures, data strategies, and AI-driven solutions to deliver measurable business outcomes. Before joining Boost Payment Solutions, Rinku led data science and engineering at Audible, an Amazon company, developing recommender models, fraud prevention tools, and compliance systems. At RBC Capital Markets, he drove data strategy and led major tech transformations in finance, risk, and regulatory reporting.

 

Mr. Sharma, from your vantage point as a CTO, what excites you most about the evolution of GenAI, and why do you think it’s such a pivotal moment for the payments industry?

At Boost, we are constantly pushing the envelope in enterprise B2B payments—building automation, eliminating friction, and delivering straight-through processing (STP) at scale.

What excites me about GenAI is how it builds on that momentum. GenAI allows us to add context, nuance, and predictive intelligence to a space that’s historically been rigid and reactive. It’s enabling us to go beyond efficiency and into areas like real-time decisioning, behavioural insights, and adaptive fraud detection. For a company like ours, already grounded in innovation, we view this technology not as a disruption, but as an accelerator.

I’m particularly excited about Agentic AI and its ability to drive a shift from automation to autonomy, where intelligent agents help us move faster, with greater precision and less intervention.

As GenAI matures, how do you envision it reshaping how businesses pay and get paid—especially in the B2B space?

The B2B payments space has historically lagged behind the consumer world when it comes to innovation, but that gap is narrowing, and Boost has played a central role in accelerating that shift. We’ve already built a robust infrastructure that streamlines and automates complex B2B transactions across industries and borders. Through Boost 100XB®, we’ve already enabled global enterprises to pay suppliers in 180+ countries using existing commercial card programs, streamlining cross-border transactions and reducing friction.

Now, we’re layering GenAI on top of that infrastructure to drive even smarter outcomes. We’re exploring AI-driven real-time optimization where the system can intelligently select the best route, method, and timing based on currency fluctuations, fees, and supplier preferences.

We’re also using AI to normalize and interpret global payment data across inconsistent formats and regulations. GenAI is helping us evolve from just seamless to truly strategic in how businesses move money across borders.

What are some of the biggest opportunities for GenAI in transforming core financial operations, like Accounts Payable (AP) and Accounts Receivable (AR)?

The opportunities lie in removing ambiguity from manual processes. On the AP side, GenAI can better understand unstructured invoice formats or emails, extracting key terms with higher fidelity. That means fewer errors, faster approvals, and tighter payment cycles.

On the AR side, GenAI enables predictive behaviour modelling, anticipating late payments or identifying trends in customer behaviour. It can even recommend collection strategies or personalised reminders. There is potential for a conversational AI platform to provide real-time responses to payment inquiries, creating a more interactive and responsive user experience for both buyers and suppliers.

Boost already has a fully built, automated straight-through processing platform. How are you using AI to take that to the next level?

One key area is advanced data parsing and translation. Our STP platform is already handling billions in payment volume, but AI helps us refine the edge cases, not just the core flow. We’re using GenAI models to improve how we extract meaning from messy, semi-structured files and emails making parsing more accurate and adaptive.

We’re also exploring how machine learning can further reduce exception handling, predict processing anomalies, and provide smarter decisioning around approvals and routing. The goal isn’t just speed, but consistency, transparency, and better operational resilience for our clients.

How do you see AI influencing the future of security, risk, and compliance in enterprise payments?

Security and compliance are core to our business. We’re focused on using it to analyse large volumes of transaction data in real time and flag anomalies before they escalate. AI can detect subtle patterns that traditional rule-based systems might miss, making fraud detection more proactive than reactive.

In the long term, we see opportunities to pair AI with distributed ledger technology to create tamper-evident records and enhance auditability. The goal is to not only detect and respond to threats faster but to build systems that continuously learn and improve.

With GenAI evolving so rapidly, how can payments innovators balance speed of development with the stability and trust that financial institutions require?

We believe innovation and trust are not at odds but interdependent. At Boost, we integrate AI capabilities in tightly scoped layers, always building on infrastructure that’s already proven. Every deployment is reviewed through our internal responsible AI framework, which includes guardrails for model behaviour, explainability, and auditability.

We also work closely with our issuer partners and enterprise clients to understand their risk thresholds and regulatory considerations. AI has to evolve responsibly. The key is transparency, governance, and ongoing monitoring, ensuring that innovation supports, rather than disrupts, the trust that financial systems are built on.

Looking ahead, what does the future of AI in payments look like—and how is Boost preparing for it?

We see a future where intelligent systems actively manage key parts of the payment lifecycle, monitoring flows, making decisions in context, and adjusting based on real-time feedback. At Boost, we’re building the foundation for that today, with a focus on trusted autonomy—AI that operates within well-defined roles and reinforces the reliability our clients expect.

We’re not trying to do everything at once. We’re methodically introducing AI in areas where it can offer clear, measurable value like exception handling, document intelligence, and anomaly detection. Over time, we expect these agents to become more capable, more embedded, and more essential to how enterprise payments run.

Rinku, thank you for taking the time to share your valuable perspectives with us.

 

*About Boost Payment Solutions

Boost Payment Solutions is the global leader in B2B payments with a technology platform that seamlessly serves the needs of today’s commercial trading partners. Our proprietary solutions eliminate friction and deliver process efficiency, data insights and revenue optimization. Boost was founded in 2009 and operates in 180+ countries.