With the current hype of AI agents in automation, business, finance, and insurance are some of the sectors actively adopting the technology. Research suggests that organisations that scale AI across their finance functions tend to perform better and achieve higher ROI. This is not surprising, as finance is considered to be the backbone of any enterprise, responsible for allocating resources to drive operations.
And with AI technology, finance workflows, and processes can be optimised through automation and content generation. Traditionally, AI automates routine, labour-intensive tasks like data collection, document summarisation, and pattern recognition.
Speaking exclusively with AIM Media House, Dominic Periera, vice president of product management at Automation Anywhere, explained that traditional or foundational models like LLMs, often provide generic responses. “The challenge is making these models more contextual and relevant to business processes,” he said.
Periera noted that LLMs can sometimes hallucinate or generate irrelevant information. To address this, Automation Anywhere uses its Retrieval-Augmented Generation (RAG) service, which grounds the model’s outputs in company-specific data and context. This could apply to various industries.
Citing the example of a commercial bank, where, a person would review about 50-page document for a loan application and analyse the terms to make a financial decision, he says it can be simplified with AI agents.
“With an AI agent trained on the same 50-page document, it can access the criteria and suggest optimal loan terms for a customer, grounded in real, company-specific data,” Periera noted.
AI Agent and Automation is Here
Automation Anywhere incorporates customised AI tools to deliver more grounded, reliable, and relevant solutions tailored to business needs and solve complex problems.
For instance, the Brazilian tax code is incredibly complex, far more intricate than India’s GST system. Automation Anywhere claims its customised AI solution resulted in huge cost savings for Petrobras, one of its oil and gas clients.
Petrobras pays over $40 billion in taxes annually. However, it struggles with frequent human errors, often overpaying or underpaying taxes. “We input the 150-page tax code into our AI model, allowing the automation co-pilot to provide accurate tax code recommendations,” Periera added. “The company’s CIO shared that within just three weeks of using the solution, they saw a benefit of $120 million. This impact is expected to grow even further.”
Similar to Petrobras, Automation Anywhere also designed an AI solution for Electrobras, a major Brazilian energy company, to solve its complex taxation issues.
What used to take days or weeks to process is now completed in hours, delivering significant value to the business.
AI Automation in Finance is the Future
As India continues to up its AI game in finance, RBI governor Shaktikanta Das recently highlighted that the integration of AI into financial services brings significant opportunities for all stakeholders.
Speaking in Bengaluru at the Global Conference on Digital Public Infrastructure and Emerging Technologies, Das said, “Today, AI has forayed into the financial sector in the form of services like chatbots, internal data processing for intelligent alerts, fraud risk management, credit modelling, and other processes.”
Also, in an exclusive interview with AIM, Gautam Singh, business unit head of WNS Analytics, mentioned that no industry will be exempt from the impact of AI. “While some, especially consumer-facing industries, will experience these changes faster, every sector, whether behind the scenes or front-facing has opportunities for both improvement and value enhancement,” he said.
So, financial services, accounting, and insurance are experiencing a lot of transformation as AI adoption is much higher in areas with dynamic and complex data. Businesses are eager to experiment with AI solutions in these fields because they rely heavily on human resources and often face challenges such as compliance risks and issues like overpayment or underpayment.
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