"The McKinsey Global Institute (MGI) estimates that across the global banking sector, AI generation could add between $200 billion and $340 billion in value annually. This is 2.8 to 4.7% of the industry's total revenue, largely through increased productivity." This was commented for the DIGI PAY blog by Anastasia Zhdanova, head of the artificial intelligence practice at Infopulse, Bulgaria.

Artificial intelligence (AI) is an important asset in all industries, integrated into various solutions to improve processes, outcomes, and profitability across the entire value chain. Industries such as financial services, telecommunications, and healthcare work with large amounts of personal and operational data. As a result, they use robotic process automation, computer tracking, and machine learning as crucial components of their operations.

How can banks make this major change?

In the banking and financial sector, AI is particularly valuable for risk reduction. AI and machine learning tools are used to assess credit and insurance risk, behavioral analysis, customer segmentation, and market analysis. AI algorithms can analyze transactions in real-time to detect and prevent fraudulent activities, thereby reducing financial losses. Additionally, AI is used in trading, virtual assistants and chatbots, credit rating, and market risk analysis.

The transformation of financial processes from automating routine tasks to providing deep insights for better decision-making is very challenging. How could banks make this major change?

The buzz around AI is undeniable, with over 3,000,000 people following #AI and #ArtificialIntelligence hashtags on LinkedIn alone.

"Transforming financial processes in banks is indeed a complex endeavor, but it can be achieved through a well-thought-out strategy," explained Zhdanova.

A successful AI journey in any sector begins with a clear understanding of the company's strategy and the definition of business value. By aligning AI initiatives with these foundational elements, banks can effectively navigate the transformation, using AI to automate routine tasks and gain deep insights for better decision-making.

What is most important in this transformation?

Generative AI (GenAI) is revolutionizing the banking sector, but Gartner Analytics predicts that 30% of GenAI projects will be abandoned after the proof-of-concept stage by the end of 2025. This underscores the importance of several critical factors for ensuring a successful transformation, emphasized Zhdanova:

• AI initiatives must be closely aligned with the overall business strategy to address the most impactful areas.
• High-quality data is essential for AI, similar to food for humans. Effective data management ensures that AI systems have the necessary information to function optimally.
• Preparing the technological infrastructure to support AI applications is crucial for seamless integration and operation.
• Ensuring compliance with regulatory requirements and managing the risks associated with AI, such as data privacy and security, is vital.

Each region has its unique challenges and opportunities, but the common thread is the strategic use of AI to drive efficiency, improve customer experience, and effectively manage risks.

Robotic process automation (RPA) plays a crucial role in automating repetitive tasks related to payment processing, such as data entry, validation, and reconciliation.

Automated compliance checks ensure that all transactions adhere to regulatory requirements, thereby reducing the risk of non-compliance penalties.

In fraud prevention, RPA continuously monitors transactions for suspicious activities, flagging potential fraud in real-time. By analyzing large volumes of transaction data, AI and RPA can identify unusual patterns that may indicate fraudulent behavior, thereby enhancing the overall security of financial operations.

By automating these critical tasks, RPA not only improves operational efficiency but also enhances the accuracy and reliability of payment processing, fraud prevention, and AML processes. This enables financial institutions to better manage risks and comply with regulatory requirements, concluded Zhdanova.

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