The Evolution of Enterprise AI in Finance

While many large enterprises grapple with outdated technologies, a wave of innovation is sweeping through the B2B payments sector, driven by agentic artificial intelligence (AI). This represents a significant leap beyond customary automation and even generative AI,promising to redefine how financial institutions operate.

Agentic AI distinguishes itself by incorporating decision-making capabilities, moving beyond mere content generation. This allows for proactive identification of trends, anomaly detection, and automated workflow management, ultimately streamlining operations and enhancing efficiency.

Consider the traditional role of AI in business. Early applications focused on automating repetitive tasks, such as data entry or report generation. Generative AI then emerged, capable of creating content like marketing copy or customer service responses. Agentic AI builds upon these foundations by adding a layer of intelligence that enables systems to act autonomously, making decisions and initiating actions based on real-time data analysis.

Expert Insights: Boost Payment Solutions Leads the Charge

Boost Payment Solutions is at the forefront of this technological shift.Rinku Sharma, chief technology officer at Boost Payment solutions, emphasizes the transformative potential of agentic AI.

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, Boost Payment Solutions

sharma further elaborates on the key differentiator of agentic AI:

It’s about intelligently acting on the data, taking decisions and creating those automated workflows that create the scale that is needed.

Rinku Sharma, Chief Technology Officer, Boost Payment Solutions

This capacity for autonomous action sets it apart from standard automation models, paving the way for significant advancements in B2B payments processing.

High-Impact Applications: Transforming Key Financial Processes

agentic AI is poised to revolutionize various aspects of banking and FinTech, including:

• Loan Underwriting: Automating risk assessment and decision-making processes.
• Fraud Detection: Identifying and preventing fraudulent transactions in real-time.
• Financial Advisory Services: Providing personalized financial advice based on individual client data.
• merchant Onboarding: Streamlining the process of bringing new merchants onto payment platforms.
• Dynamic Pricing: Optimizing pricing strategies based on market conditions and customer behavior.
• Transaction Processing: Enhancing the speed and efficiency of payment processing.

Boost’s AI Journey: From Rules-Based Systems to intelligent Automation

Boost Payment Solutions’ journey with AI began with traditional rule-based automation and has evolved to embrace the capabilities of large language models and agentic AI. Sharma notes:

We are in very early stages so far, like everybody else. Though, 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.

Rinku Sharma, Chief Technology Officer, Boost Payment Solutions

As a FinTech acquirer focused exclusively on the B2B marketplace, Boost processes a high volume of payment information daily. Scaling this system effectively requires intelligent automation, and agentic AI is proving to be a crucial component.

Use Cases in Action: Reconciliation Reporting, Data Validation, and More

Boost is leveraging agentic AI in several key areas:

• Reconciliation Reporting: While GenAI can create reports, agentic AI proactively detects anomalies, alerts stakeholders, and initiates workflows to resolve issues.
• Data Validation: automating the validation process and ensuring compliance with regulations like Know Your Business (KYB).
• Merchant Onboarding: Streamlining the traditionally manual and data-intensive onboarding process. Sharma explains: Merchant onboarding has historically been one of the more heavy, manual touchpoints. it requires a lot of data gathering,setting up merchants and following up for additional information.
• Dynamic Price Optimization: Enhancing existing rules-based systems to make real-time decisions based on transaction cost, currency, customer preferences, and other variables. Sharma states,We’re looking to leverage agentic AI to enhance that further and make those decisions in real time. It’s about finding the best route for a transaction and continuously improving the process.

The Importance of Data Quality and Accountability

Sharma emphasizes that the effectiveness of agentic AI hinges on the quality of the data it receives. the models are only as good as the data being fed to them. Garbage in, garbage out holds true even with agentic AI. Boost ensures data quality through multiple layers of validation, enrichment, and standardization.

Accountability is another critical factor. We want to make sure that these models are not just smart, but they’re accountable as well. Validating the model outputs is what we believe in. This commitment to accountability ensures that AI-driven decisions are reliable and trustworthy.

Looking Ahead: The Future of B2B Payments with Agentic AI

As Boost continues to scale its operations,Sharma remains committed to 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.

Sharma envisions a future where agentic AI is a foundational component of the B2B payments industry. 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.