The Future of AI in Banking

Digital render of a classic bank building

AI in Banking: The Road Ahead

Scroll to explore how AI is reshaping banking

AI is transforming the financial sector at an unprecedented pace.  

Banks are expected to invest $85 billion in AI by 2030, a trajectory with a 55% compound annual growth rate. AI is already reshaping fraud detection, loan approvals, and risk management, while Generative AI (GenAI) is augmenting employees at all levels.

Let's look at some important data

We are developing models to help us better prepare for adverse scenarios in the next five years. So, if an economic crisis arises, we can more effectively mitigate the effects of a rise in defaults. José Miguel Pessanha  Millennium BCP Bank

GenAI can fuel innovation by generating new ideas and solutions, helping us stay ahead of the competition and adapt to changing market conditions. Andrea Cosentini Intesa Sanpaolo

The Impact of GenAI in Banking

Banks that have deployed GenAI report the following impact:

of banks have deployed Gen AI use cases.

Noticed an uptick in customer satisfaction.

Report improved employee satisfaction.

Experience increased operational efficiency.

Source: Your journey to a GenAI future

To learn more about this ethos for innovation, we interviewed four banking leaders.

Visionaries who are shaping the future of AI in banking

Hover over a card below to see what they had to say about us.

José Miguel Pessanha 
Millennium BCP 

José Miguel Pessanha is Chief Risk Officer at Millennium BCP, overseeing the offices of risk, compliance, regulatory monitoring, and data protection. He holds master’s degrees in economics and operations research.

Abraham Izquierdo 
Banorte

Abraham Izquierdo is Managing Director at Banorte, overseeing the Risk Policy, North Region Credit, and Assets & Liabilities Committees. He also serves as secretary of the Derivatives Committee and chairs the Investment Services Committee. He holds master’s degrees in risk, finance, and business.

Andrew McCammack
Old National Bank

Andrew McCammack is Old National Bank’s Data Science Officer. He has been with the bank for nearly a decade, helping the organization turn data into actionable intelligence.

Andrea Cosentini 
Intesa Sanpaolo 

Andrea Cosentini is Intesa Sanpaolo’s Head of Data Science & Responsible AI. He has a degree in physics and a PhD in mathematical and statistical methods for economics.

Visionaries shaping the future of AI in banking

José Miguel Pessanha 

José Miguel Pessanha is Chief Risk Officer at Millennium BCP, overseeing the offices of risk, compliance, regulatory monitoring, and data protection. He holds master’s degrees in economics and operations research.

Abraham Izquierdo

Abraham Izquierdo is Managing Director at Banorte, overseeing the Risk Policy, North Region Credit, and Assets & Liabilities Committees. He also serves as secretary of the Derivatives Committee and chairs the Investment Services Committee. He holds master’s degrees in risk, finance, and business.

Andrew McCammack

Andrew McCammack is Old National Bank’s Data Science Officer. He has been with the bank for nearly a decade, helping the organization turn data into actionable intelligence.

Andrea Cosentini

Andrea Cosentini is Intesa Sanpaolo’s Head of Data Science & Responsible AI. He has a degree in physics and a PhD in mathematical and statistical methods for economics.

What can you learn from fellow banking leaders?

Fundamentals of a successful AI journey

For the banking sector, the AI opportunity is not about a particular technology or algorithm. It is about recognizing the imperative of completely integrating AI with core business strategy. This has emerged as a real ethos among AI leaders in the sector as they continue to build long-term reliance and competitiveness.

Scroll to see the five key insights about AI in banking from our featured banking leaders.

We can make strategic decisions for the short, medium, and long term based on data and modeling. Abraham Izquierdo Banorte

If you have a platform that, behind the scenes, everything was designed to work together, and then you put AI on top of that; it's easier for AI to deliver greater value, faster. Andrew McCammack Old National Bank

AI in Banking: 5 Insights That Matter

Key Insight 1

Create a strong business case while building confidence in measured steps.

AI has a role to play in reducing risk, improving the way new products and services are delivered and ensuring optimal efficiency regardless of how quickly a business grows and diversifies or how volatile a marketplace might be.

However, the need to deploy and scale new technology at pace must be weighed against the need to ensure that all stakeholders feel confident and vested in the journey. Among AI leaders in the banking sector, we see innovation leaders building consensus among the C-suite.

Key Insight 2

Take a people first approach with strong leadership.

AI is helping senior decision makers combine their experience and problem-solving abilities with rich data-led insights to shape decisions that fuel growth, minimize risk and fortify resiliency. This approach provides a powerful opportunity for leaders to share their learnings in an open, transparent way and, in turn, encourages their teams to learn that AI and cloud deployments must be multi-year and step by and adapt.

Key Insight 3

Establish a strong technological foundation.

Leaders suggest starting small with discrete AI use case trials. Incremental success builds confidence.

Solving business challenges with AI requires a life cycle that spans from managing and cleaning data to building and optimizing models to deploying and automating insights.

The productivity of data and AI teams navigating this life cycle are often challenged by time, complexity and resource requirements.

Key Insight 4

Empower your IT and developer teams.

For the organizations that are leading in their sector in terms of AI deployment, there’s an increasing focus on building an organization’s own capacity for innovation.

The question becomes less about what technology to purchase and more about how best to leverage technology to augment the ingenuity of technology and developer teams.

Key Insight 5

Listen and learn from technology partners.

The leaders we talked with are committed to staying attuned to developments outside of the organization and even outside of the banking sector. This is because the present-day pace of global innovation is unprecedented. All of the ways technology such as AI and GenAI can be used to increase revenue and profitability grows daily.

AI in Banking: 5 Insights That Matter

Key Insight 1

Create a strong business case while building confidence in measured steps.

AI has a role to play in reducing risk, improving the way new products and services are delivered and ensuring optimal efficiency regardless of how quickly a business grows and diversifies or how volatile a marketplace might be.

However, the need to deploy and scale new technology at pace must be weighed against the need to ensure that all stakeholders feel confident and vested in the journey. Among AI leaders in the banking sector, we see innovation leaders building consensus among the C-suite.
Key Insight 2

Take a people first approach with strong leadership.

AI is helping senior decision makers combine their experience and problem-solving abilities with rich data-led insights to shape decisions that fuel growth, minimize risk and fortify resiliency.

This approach provides a powerful opportunity for leaders to share their learnings in an open, transparent way and, in turn, encourages their teams to learn that AI and cloud deployments must be multi-year and step by and adapt.
Key Insight 3

Establish a strong technological foundation.

Leaders suggest starting small with discrete AI use case trials. Incremental success builds confidence

Solving business challenges with AI requires a life cycle that spans from managing and cleaning data to building and optimizing models to deploying and automating insights.

The productivity of data and AI teams navigating this life cycle are often challenged by time, complexity and resource requirements.
Key Insight 4

Four Futures of Insurance: Strategic Insights from Economist Impact Research

For the organizations that are leading in their sector in terms of AI deployment, there’s an increasing focus on building an organization’s own capacity for innovation.

The question becomes less about what technology to purchase and more about how best to leverage technology to augment the ingenuity of technology and developer teams.
Key Insight 5

Listen and learn from technology partners.

The leaders we talked with are committed to staying attuned to developments outside of the organization and even outside of the banking sector. This is because the present-day pace of global innovation is unprecedented. All of the ways technology such as AI and GenAI can be used to increase revenue and profitability grows daily.

Your journey to a Gen AI future starts now

There’s no doubt that the financial services sector has emerged as a leader in AI adoption. For those who take the right approach, the value is clear and compelling.

AI’s fundamental value lies in its ability to analyze massive amounts of data and providereal-time insights. And we see this among common use cases within the banking industry – from improving fraud detection to having a greater ability to make real-time decisions at the time of loan applications.

Learn more from our experts in the full report.

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