Five Ways to Achieve Banking Success with Data and AI

Banks are on the cusp of a transformative shift as they increasingly turn to data and artificial intelligence (AI) to gain a competitive edge in the rapidly evolving financial industry. This strategic move isn’t just about adopting the latest technology; it’s a fundamental change permeating every aspect of banking, underscoring the potential for exponential growth and success that these technologies can bring.

As customers become more tech-savvy and gain confidence in using AI-powered fintech apps, they realise that technologies can significantly improve their user experience with banking products and services. Many of these new technologies are data-driven, so banks must find ways to deliver them using their resources.

Data fuels the engine of innovations in Banking

However, AI technology goes far beyond chatGPT and is the way to use the data we’ve been discussing to get real business results. Banks are leveraging data and AI for success in many innovative ways: personalized customer experiences, enhanced security measures, operational efficiencies, predictive analytics, and competitive market analysis. AI-powered tools enable banks to analyze customer behaviour and competitor strategies, providing valuable insights for strategic decision-making and competitive advantage. All of this helps to monetize data and create new revenue streams for the financial industry.

Data is the new Eldorado

Data monetization can occur in many ways in our daily interactions with financial services providers. A good example is data mining combined with appropriate algorithms to personalize offers and rates to improve service for a 360-degree view of customer behaviour and personal needs based on all interaction points with the bank. Even such trivial information as logs, records in databases, documents or a technical issue, usability analysis of how often a user has received an error, or what type of error, in their mobile banking application provides insights and gives a quick hint to decide what to change or needed updates of certain features.

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By adopting a “data as a product” strategy, banks can provide relevant analytics to the corporate customer segment and create a marketplace for anonymized data. The EU has strict regulations on the handling and managing personal data, and banks strictly enforce compliance requirements, so any fears of data “misuse” are completely unfounded.

Let’s illustrate five use cases where financial organizations can start harnessing data with almost immediate effect and deliver excellent experiences to all stakeholders

Personalized Customer Experiences

Personalized customer experiences are a cornerstone of successful banking, and banks are leveraging data and AI to create these experiences. According to a report by McKinsey & Company, personalized services can lead to a 10-15% revenue increase for banks. Overall, companies that seize the value of personalization will grow faster at an above-average rate and potentially see up to 40% more revenue from personalized actions and tactics [1]. Banks analyse customer data to understand individual preferences, behaviours, and needs. AI-enabled solutions then process this information to offer tailored product recommendations and services, enhancing customer satisfaction and loyalty.

Fraud Detection and Security

Banks are turning to advanced AI algorithms in cybersecurity to detect and prevent fraudulent activities in real-time. A Deloitte survey underscores AI’s crucial role in identifying anomalies and unusual patterns that could indicate fraud [2]. By integrating machine learning models, they continuously learn from historical data, adapting to new threats and evolving security challenges.

The market has welcomed innovative real-time decision solutions, helping to improve the speed and accuracy of anti-fraud solutions. The new generative AI technology analyzes one trillion data points to predict a transaction’s authenticity in real-time, including account, purchase, merchant, and device information[7]. Therefore, GenAI protects banks and customers from fraudulent events and builds trust in every interaction. Taking this proactive approach safeguards customers’ interests and strengthens every bank’s reputation for security and trustworthiness.

Operational Efficiency and Cost Savings

AI is a key driver for banks seeking to optimize resources and reduce costs through operational efficiency. Banks can streamline routine and time-consuming tasks by leveraging data-driven insights and AI automation, allowing employees to focus on more complex and strategic activities. Intelligent tools automate monitoring and control and support decision-making through smart data management. According to a research paper, AI-driven automation in banking operations might lead to a 22% cost reduction [3]. By 2030, it is predicted that AI harnessing data will save up to 1 trillion USD for the financial industry. This efficiency enhances productivity and contributes to a more agile and responsive organizational structure.

Predictive Analytics for Financial Decision-Making

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Predictive analytics powered by AI is reshaping how banks make financial decisions. By analysing vast datasets, AI models predict trends, customer behaviours, and potential risks. This foresight enables banks to make informed investment decisions, portfolio and risk management, and loan approvals. In an exploration of predictive analysis for loan modelling, the accuracy is estimated to be as high as 99.2% [4]. Banks can improve their loan approval accuracy by implementing predictive analysis, which mitigates risks and improves the institution’s overall financial health.

Competitive Market Analysis

Staying competitive in the banking industry requires a keen understanding of market trends and competitors. Data analytics and AI play a critical role in providing actionable insights. According to a 2024 study by Accenture, 42% of consumers find it difficult to differentiate between financial services brands [5]. The market landscape is certainly not easy to predict. However, by leveraging AI technologies, banks can analyze market dynamics, assess competitors’ strategies, track website visitors and their online behaviour, and then use all the information to adapt or personalize their own approach to customers. AI can enable differentiation through unique personalization with the right offer for bank customers.

Finally, achieving business success in the contemporary landscape requires integrating data and AI into core operations[6]. The examples discussed – personalized customer experiences, fraud detection, operational efficiency, predictive analytics, and competitive market analysis – showcase the transformative power of these technologies. As banks continue to innovate and adapt, the symbiotic relationship between data, AI, and success in the financial sector becomes increasingly evident. By embracing these technologies, banks future-proof themselves and enhance customer satisfaction, ensure security, and gain a significant advantage in an ever-competitive market.

To step confidently into the world of data and AI, the Sirma team offers you a technology partnership to achieve goals and succeed in Data monetization and AI integration. Our domain consultants and IT experts are keen on taking on a new quest to utilize your Data silos and convert them into actionable insights with AI. Book your free meeting here.

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