When AI meets banking

Banking ai

The new revolution, when AI meets Banking. Over the decades, the banking industry has steadily evolved by introducing significant changes, from payment cards in the 1970s through online banking in the early 2000s to today where the next revolution in the industry will be the consolidation of the use of AI. Looking at the main areas of implementation of AI-based tools in this sector, it is easy to assume that in the coming year’s AI will bring significant benefits in three main directions:

  • Increase revenues by defining targeted offerings;
  • Reduce costs by limiting operational errors and making processes more efficient;
  • Identify new application areas based on AI’s potential in data analysis.

When AI meets banking: the strategic approach

Every year, billions of euros are spent in banking to implement new technologies, but only a small percentage of banks have succeeded in extending the use of AI-based software to all levels of the structure and key processes. The first obstacle to the real diffusion of the use of these technologies is the lack of a common strategy among the various divisions, but the presence of an obsolete operational and/or technological model also has a negative impact. Current bank IT environments are designed as robust systems, but inflexible and even less able to support the current data processing and real-time analytics requirements of continuous loop artificial intelligence applications.

When AI meets banking: data management

In addition, many banks’ data management is split between multiple repositories (with so-called siloed structures), and analytics efforts are narrowly focused on individual needs and use cases, without a comprehensive view. The lack of insight, therefore, makes it difficult to analyze relevant data and be able to generate recommendations and smart offers at the right time. Data is potentially the primary source for identifying new business opportunities, and it needs to be governed and made securely available by enabling the analysis of data from multiple sources at scale for millions of customers, in real-time. To use sophisticated mathematical-statistical and AI analytics models, organizations need a robust set of tools to test and monitor the efficiency of these analytics in an increasingly effective and scalable way.

When AI meets banking: digital transformation

To overcome these barriers, banks must invest in digital transformation, creating a widespread culture of change. To define and implement robust AI-based decision-making processes, banks will need to move from attempting to develop AI tools for specific use cases to evolving an enterprise-wide approach for all application domains. To enable large-scale development of AI-based decision-making models, banks must start by standardizing AI processes, share across all levels the new analytics paradigms, and use tools that can support tasks with this level of complexity.

When AI meets banking: change management

The final step lies in being able to explain the results of AI to end-users through a change management plan that succeeds in changing the mindset of employees and making up for deficiencies in certain skills. Only banks that manage to take this path, conceiving AI not only as a trend but as a crucial point within every business decision, will be able to gain a real competitive advantage in the market.

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