AI/ML - Transforming Banking Services

The adoption of AI/ML in banking is not just a technological advancement; it is a strategic imperative that is reshaping the industry. From personalised consumer banking experiences to efficient commercial operations, AI/ML technologies are driving significant improvements in customer satisfaction, operational efficiency, and financial performance.

As investment in these technologies continues to grow, the banking sector will undoubtedly experience even more profound transformations, paving the way for a more innovative and customer-centric future.

The banking industry’s investment in AI/ML technologies has been substantial and is expected to grow significantly. According to IDC, global spending on AI systems in the financial sector is projected to grow to $22.6 billion by 2025.

From reducing wait times, to avoiding unsatisfactory call centre experiences, faster transaction processing and the ever-present threat of fraud, there are several challenges that today’s banks and their customers face, which can be alleviated by AI, to deliver personalised experiences, streamlined operational processes and secure banking.

With the advent of sophisticated algorithms and data analytics, AI/ML technologies are enabling banks to enhance customer experiences, optimise operations, and maintain competitive edges in an increasingly digital marketplace.

The wide and varied AI/ML driven interventions in banking can be classified under five key themes or pillars that constitute the strategic initiatives deployed by banks, which provide a profound impact on operations, enhancing efficiency, security, and customer satisfaction.

Customer Experience: Consisting of building a highly personalised and customer-centric environment, backed by self-service platforms to empower and enable customers to manage their finances conveniently.

Credit Decisioning: Providing advisory and early warning signals to predict credit risk and enable precise assessments of borrower creditworthiness, leading to better decision-making and reduced default rates.

Campaign & Channel Management: Automating marketing efforts and optimising channel strategies ensuring that customer receive targeted and relevant offers, boosting engagement and conversion rates.

Operational Efficiency: Streamline workflows and reduce manual intervention, allowing banks to optimize and automate services, reducing operational overheads.

Risk & Regulatory Evolution: Comply with evolving requirements and mitigating risks, by automating risk assessments and regulatory reporting

84% faster growth of deposits for banks with the highest customer satisfaction ratings

Continuous monitoring and optimisation are crucial for the success of AI/ML deployments. Banks should regularly evaluate the performance of their AI models, gather feedback, and make necessary adjustments to improve accuracy and efficiency.

Deploying AI/ML requires expertise in data science, machine learning, and AI engineering. Banks should invest in building skilled teams or partnering with technology firms to access the necessary talent.

According to Allied Market Research, the AI market in banking is projected to grow from $160 billion in 2024 to $300 billion by 2030?

This dramatic increase reflects how essential AI and ML are becoming in credit risk management.

AI systems can process thousands of data points in a fraction of the time it would take human teams, ensuring that no red flag is missed. It shifts the focus from reactive responses to proactive decision-making.

Predictive analytics can foresee a surge in non-performing loans (NPLs) based on emerging economic indicators, allowing banks to adjust their portfolios before experiencing losses.

As AI & GenAI reshape how banks handle risks and compliance, these technologies are transforming traditional strategies. Banks are now able to predict risks, automate compliance checks, and manage regulatory challenges proactively, thus safeguarding their reputations and improving financial stability.