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DataAIUse casesSuccess stories
AI has been around in banking for a while — banks were actually early adopters. Back then, it was all about recognizing patterns in past data to figure out why certain things happened or predict what might come next. But as the volume of data skyrocketed, customers demanded personalized experiences, and cybersecurity threats grew more sophisticated, real-time insights became crucial. That’s when banks realized they needed stronger tools to keep up and stay in the game.
GenAI became the solution. Powered by deep neural networks and LLMs, it can now independently create meaningful outputs and generate synthetic data that draws from real-world datasets. This turned out to be a game-changer for boosting productivity, catching fraud, leveling up customer service, and speeding up decision-making.
Not on the GenAI train yet? Find out how it’s already shaking things up in banking and why you might want to jump on board!
the potential annual savings GenAI can bring to the banking sector
the projected growth in GenAI spending by the banking industry by 2030
GPTs are powerful language models trained on massive amounts of data, designed to understand and generate human-like text with impressive accuracy. In banking, they can drive customer service chatbots, streamline financial report generation, and offer natural language interfaces for easy tasks like checking balances and transferring funds.
GANs use two neural networks — a generator and a discriminator — that work against each other to produce high-quality synthetic data that closely mimics real-world data. In banking, GANs can be used to train fraud detection models, simulate realistic financial scenarios for stress tests, and create synthetic identities for testing anti-fraud systems.
VAEs compress data into a latent space and reconstruct it back to its original form. In banking, VAEs can help spot unusual transactions by comparing recreated data with real data to catch fraud, create new customer profiles to better target different segments, and boost credit risk models by generating extra data to improve predictions.
GNNs are built to handle and analyze graph-structured data. They look at how different things interact and can create new graph structures within the network. In banking, GNNs are used to analyze and generate transaction networks to detect fraud or money laundering, map customer relationships, and optimize supply chain networks.
RL models learn to make decisions by interacting with an environment to maximize cumulative rewards. When combined with generative components, RL can be used in banking to create adaptive trading strategies, optimize investment portfolios, and improve credit risk management by generating borrower behavior models.
GenAI isn’t just another tech upgrade for banks — it’s set to completely revolutionize how they operate and even spark new business models. Banks are already applying it in so many areas that soon nearly every part of banking will feel its impact.
“The integration of GenAI in banking is set to shake things up in a big way. For banks, it’s no longer about if AI will make a huge impact — it’s about how. The biggest players in the industry are already shifting gears with GenAI, and the initial results are nothing short of amazing.”
Chief Delivery Officer & Head of Competence Center
GenAI’s ability to manage vast amounts of data, automate processes, and generate strong insights gives banks valuable advantages that help them operate more effectively and stay competitive.
GenAI simplifies operations by automating tasks like data analysis, report generation, and document processing. This makes banks more efficient and improves credit risk assessments and fraud detection.
GenAI can spot potential risks early and more accurately, giving banks a heads-up to adjust and minimize losses. Bankers use predictive insights to safeguard assets and seize market opportunities.
GenAI automates tasks like risk assessment, compliance checks, and handling customer inquiries — meaning banks spend less on staff and run more efficiently. It also uses predictive analytics to help banks allocate resources and cut down on investment risks.
GenAI tools help with strategic decision-making by analyzing market trends and financial data, and testing different market scenarios. They propose and evaluate new trading strategies to help banks spot profitable opportunities and minimize losses.
With GenAI, banks can quickly design and test new products. The technology helps whipping up prototypes faster and roll out innovations sooner. Plus, GenAI learns from customer feedback and market trends to keep banks improving and fine-tuning their products.
As banks get bigger, manual tasks and hiring more staff can really drive up costs. But with GenAI, banks can scale up and manage more work — like processing loans or handling customer questions — without proportional increases in staffing.
While GenAI use cases are looking promising and exciting, it’s going to take some time before we see its full impact on the banking industry. Banking leaders, especially when dealing with limited tech and resources, will need to tackle some big challenges and concerns before they can roll it out on a larger scal
GenAI runs on data, and with a lot of data comes a big responsibility. Banks have to make sure they’re keeping customer data safe and private. If they mess up, it could lead to data breaches and hurt their reputation. The tricky part is that regulators are having a hard time keeping up with how fast AI is moving, which may result in inconsistency in privacy and security rules.
To tackle this, banks should think about setting up solid data governance frameworks prioritizing data anonymization and encryption. By keeping an eye on privacy regulations and tweaking their GenAI strategies, they can boost compliance and strengthen their overall data management game.
Old tech is another thing holding back the commercial use of GenAI. These outdated systems make it harder to bring in new, innovative features. For starters, they often use old data formats and protocols that don’t work well with modern AI. Plus, they tend to store data in isolated or proprietary formats, making it tough to access and use for GenAI training and analysis.
Considering the hefty price tag of a complete system upgrade, banks can start by upgrading specific components of their legacy systems, exploring data integration tools for better data access, and implementing basic data cleansing practices to provide high-quality inputs for GenAI applications.
One of the biggest worries for banks with GenAI is the risk of bias and unfairness. If the data used to train the AI is incomplete, the results can be skewed and lead to unfair lending decisions for certain groups. Plus, GenAI can confidently produce wrong answers, known as “hallucinations.” These made-up but realistic-looking results can be a huge problem in banking.
A smart move for banks is to use RAG (Retrieval-Augmented Generation) technology. This lets them feed AI reliable data, making sure it produces accurate answers instead of creating misleading ones. Regular audits of AI models and using diverse training datasets can help spot and reduce biases and keep lending practices fair.
The talent shortage is another hurdle for GenAI adoption in banking. Bringing AI into the mix will shake up many jobs, meaning employees will need to learn new skills or even switch roles. Banks will have to figure out whether to retrain current staff or hire new people with the right skills.
Encouraging employees to take on new roles within the organization can help keep talent in-house while filling those gaps in the AI space. Banks might also want to consider teaming up with tech companies that really know their stuff when it comes to AI. These partnerships can provide valuable expertise, training resources, and fresh ideas to help level up their team’s skills.
Bringing GenAI into banking takes some careful thought and planning. Here are the key tips to help you set things up for a successful GenAI rollout.
“The real roadblock in adopting GenAI is thinking it’s just too complex to handle. And sure, it can be — but with the right experts in your corner, it doesn’t have to be. We’re here to help you build the right GenAI foundation from the ground up — identifying your pain points, spotting opportunities, and advising you on the best tech to get the job done.”
Chief Delivery Officer & Head of Competence Center
GenAI in banking is evolving quickly, with new use cases popping up every day. This tech has the potential to completely reshape the industry. Those who jump on board are gearing up for new revenue streams and higher efficiency. According to the McKinsey Global Institute, GenAI could boost global banking revenues by 2.8% to 4.7%, mostly thanks to productivity gains.
It’s clear that GenAI isn’t just a trendy buzzword anymore — it’s becoming a must-have for banks. In fact, spending on GenAI in banking is expected to soar from $6 billion in 2024 to a massive $85 billion by 2030, according to Juniper. With this kind of investment, GenAI is set to revolutionize banking operations and deliver more secure, efficient, and personalized experiences for customers.
GenAI is quickly changing the game in banking, tackling problems that traditional tech just couldn’t handle. Some banks are already diving in, using GenAI to cut costs, personalize customer experiences, and boost efficiency. Others are still testing the waters, mostly using it to automate routine tasks that used to need a human touch. But that’s just the beginning.
The future of GenAI holds surprises, but one thing’s for sure — the real opportunity comes from pushing past the basics and embracing everything GenAI has to offer. Are you ready to take it on?
Banks use a "human-in-the-loop" approach that helps catch any mistakes or anomalies before they can cause problems. By using AI to generate initial responses and then creating feedback loops with human input, banks can fine-tune the model and get closer to 100% accuracy.
GenAI helps with fraud prevention by being super adaptable. It learns from new data and keeps updating its fraud detection algorithms, so it stays sharp against both familiar and new threats. It cuts down on false alarms, so that real transactions don’t get wrongly flagged as fraud.
With GenAI, banks can analyze large amounts of unstructured data to predict trends and assess market risks. This boosts risk management, cuts down on market volatility exposure, and strengthens regulatory compliance, leading to better financial performance and higher returns.
Global Development Director
Ivan orchestrates complex, multi-regional development operations. He focuses on resource optimization and engineering discipline, ensuring that large-scale technical projects remain aligned with business objectives while maintaining a relentless pace of delivery.
