RPA and AI in Finance: Opportunities and Challenges

The Current State of Automation in Financial Services

Financial institutions such as banks, insurance companies, and fintech startups operate in an environment of intense competition. To succeed, they must continuously seek ways to reduce operational costs, improve service quality, and meet evolving customer expectations. Today, being a successful financial company requires more than offering good products and services. It demands a high level of technological maturity to maintain at least average profitability compared to peers.

Adopted by over 30% of banking, finance, and insurance organizations, Robotic Process Automation (RPA) has become:

• A proven solution with mature implementation cases
• A key driver of digital banking transformation
• A catalyst for business process automation
• A component of a hyperautomation market that grows at over 25% CAGR

RPA has reached a high level of maturity, increasingly integrating AI to address complex tasks. RPA and AI help financial institutions adapt faster to regulatory changes, accelerate time-to-market for new services, and detect suspicious transactions more efficiently than manual methods, reducing financial risks. Automated lending processes are another clear example of value creation.

Processes that involve large data volumes or repetitive tasks are prime candidates for RPA. However, successful automation requires careful ROI analysis, a comprehensive automation strategy, and a phased project roadmap. Using this approach, organizations can move toward fully automated end-to-end processes, where RPA plays a significant role alongside Business Process Model and Notation (BPMN) and Artificial Intelligence (AI).

Strengthening Risk and Compliance with Automation

Risk management is critical for any financial institution and intelligent automation significantly strengthens this capability. Automating compliance checks, risk monitoring, and fraud detection, RPA reduces errors and accelerates response times, which is especially important in a shifting regulatory environment.

For example, automation solutions can:

• Reduce error probability by up to 90%
• Increase the speed of task execution by more than 50%

These benefits make RPA an effective tool for enhancing compliance and risk resilience.

Cost Optimization and Trust Building

RPA is widely recognized for its ability to reduce costs and deliver strong ROI, often three to ten times higher depending on the use case. On average, financial organizations can achieve cost savings of 30% or more.

Beyond cost optimization, RPA supports strategic initiatives. Automating compliance tasks, it helps institutions stay aligned with regulatory requirements, ensuring smoother audits and reporting. It also strengthens risk management, thereby increasing customer trust and reinforcing the organization’s reputation for reliability.

Use Cases Delivering Innovation and Competitive Advantage

Our experience comes from working with banks and insurance companies worldwide, where we have implemented comprehensive RPA and AI-driven solutions. These projects often combine process automation with robust data infrastructure, including data mining, data lakes, and advanced analytics.

The examples of the projects include:

• An automated customer support system capable of resolving over 90% of requests
• A credit scoring solution that improved a bank’s lending profitability by 34%

These solutions illustrate how RPA and AI enable the creation of digital-first institutions ranging across neobanks and AI-powered banking models. At IBA Group, we place strong emphasis on regulatory compliance, data security, and building secure IT environments. In complex AI projects, we leverage trusted platforms such as Nvidia to ensure both performance and reliability.

Balance Between Automation and Human Oversight

Intelligent automation has already proved to be highly effective in replacing manual work, particularly for routine tasks. However, human involvement remains essential for approvals, exception handling, and oversight.

In modern digital and neobanking models, robotized processes already operate with minimal human intervention. With the combined use of RPA, BPMN, and AI, automation levels can reach 75% or higher in banking, insurance, and advanced fintech companies. Human oversight will remain, but its scope will gradually narrow as automation evolves.

Addressing Cybersecurity and AI-Related Risks

AI-enhanced RPA (often referred to as Intelligent Process Automation, or IPA) brings new efficiency gains but also increases exposure to cyber risks. Mitigation requires a layered and proactive approach.

At IBA Group, we build IT landscapes with secure data collection, storage, and advanced analytics, often leveraging Nvidia-based solutions. We also emphasize access management, penetration testing across geographies, and strict data governance.

This approach enables organizations to:

• Prevent unauthorized data entry
• Apply rigorous data filtering policies
• Meet security compliance standards
• Minimize operational errors

Our experience shows that such practices can increase IT system security by several times, depending on baseline conditions.

The Next Frontier for RPA and AI in Finance

RPA has already evolved into Intelligent Process Automation, where AI is integrated to handle unstructured data and complex workflows. The next step is broader adoption of AI-first models, requiring financial institutions to modernize their IT landscapes for long-term competitiveness.

The most transformative trend is the use of AI across core financial operations:

• AI-driven risk management
• AI-powered customer onboarding and compliance
• AI-based fraud detection
• AI-enabled personalized offers and smart recommendations

These developments will shape the next generation of AI banks, insurers, and fintech companies. AI has already become a cornerstone of the industry and will only grow in strategic importance.

Achieving the Right Balance Between Speed and Reliability

The first step is to optimize processes before automating them. Removing inefficiencies and bottlenecks, organizations can achieve more effective and scalable automation. It’s also important to select processes for automation with care, focusing on rule-based, repetitive, and time-consuming tasks.

Thorough testing is indispensable in financial environments. Functional testing, exception handling, and integration testing must all be performed before go-live. At IBA Group, we develop comprehensive testing plans that also include regression testing automation to accelerate validation while ensuring system reliability. This disciplined approach enables financial institutions to implement automation quickly and without compromising stability or trust.

Before automating any task, it is essential to analyze and optimize the existing processes. This way, we eliminate inefficiencies and bottlenecks, and ensure that automation can be implemented with scalability and precision. Not every process is suitable for automation, so the focus should remain on rule-based, repetitive, and time-consuming tasks.

Thorough testing and validation are critical before deploying automation solutions in a production environment. Functional testing, exception handling, and integration testing must all be part of the plan to minimize errors and disruptions. At IBA Group, we also use automation for regression testing, which accelerates validation and supports smooth transition to large-scale automation.

For examples of IBA client success stories, including RPA, please click here.  For more information on technology strategy and how tech connects to real business solutions, please click here.