Polski
Deutsch
Data analytics in banking are critical in a number of scenarios. Not only do they help segment customers and provide them with personalised customer service, but also plan investments, evaluate credit scores and risks, or even improve internal processes. Artificial intelligence helps in all of these cases. Therefore, in this article, we shall focus on its role and use cases. Are you interested in this topic? Then we invite you to read on!
Table of Contents
- Why Introduce AI-Powered Data Analytics in Banking?
- AI-Powered Data Analytics in the Banking Industry: Practical Use Cases
- Conclusions
Why Introduce AI-Powered Data Analytics in Banking?
Artificial intelligence is a powerful tool in data analytics – it comes with unique advantages that are unachievable by any other means. Thus, to begin our article, let’s look at the possibilities and benefits of AI-powered data analytics for banking.
Multi-Dimensional Approach
The first major advantage of an AI data analyst in the banking sector is that it looks at the data as a whole, no matter the size of the dataset. Due to its high data-processing capabilities (limited only by modern computing power), it may take into account significantly more data at once than even a whole team of human analysts. What opportunities does this open?
Mainly the possibility of finding new correlations and patterns. An AI-powered system spots even seemingly unrelated correlations and prepares insights based on them, helping financial organisations see a broader picture and improve the processes even further.
Naturally, this comes with some risk. In this case, data bias might be the perpetrator. Due to it, your system might, for instance, link certain financial aspects with parameters like… gender or race, discriminating against certain social groups. Therefore, it is crucial that your system works on unbiased, carefully cleansed data.
At Ailleron, we’re the fintech experts you need to design your ML model. We will help you create a system tailored to the specifics of your organisation!
Data Processing Speed
AI is also faster than humans when it comes to processing large data volumes. Due to this, it becomes possible to analyse data in real time. Why is this important?
First of all, it’s an excellent tool for investments. An AI-powered system can analyse the market constantly to find indicators of great opportunities and let you use them to your advantage. Secondly, this can be implemented in personalised product recommendations, especially in the future, to analyse the context in which your customers are. Finally, this is a key feature for big data analysis – with AI engines, you no longer need to wait weeks to make company-level decisions; instead, you’ve got all the important information served on a silver platter, waiting for you to draw conclusions and decide based on them.
Predictive Insights
Another key advantage of AI-powered data analytics is that your artificial intelligence can spot patterns in historical data and react when the same patterns occur in your current information. In practice, this means providing your organisation with predictive insights that you can act on.
This can be both large-scale – regarding company-level decisions – and small-scale – regarding, for example, predicting customer churn and preventing it. Therefore, it’s a feature that you need to embrace.
Personalisation
Delivering personalised customer experience is crucial in the third decade of the 21st century. Once again, artificial intelligence used for data management and analytics can help you with that.
Here, the case is simple – an AI model may use structured (but also unstructured data) to analyse a particular customer. Based on this data, your AI engine (or your employees) may deliver personalised content and product recommendations or even adjust the services or… the layout of your client’s app. After all, to personalise, you need to understand your customers, and artificial intelligence enables you to do that.
Scalability
Although scalability is kind of a buzzword in business, we mustn’t overlook it in the banking data analytics sector. Before AI, your team needed to grow along with the amount of data; with AI, this is no longer an issue – your system can process so much information that the growing number of it will not be a problem (unless it exceeds modern computing limits).
AI-Powered Data Analytics in the Banking Industry: Practical Use Cases
Having established the main advantages of AI-powered data analytics, let’s look at several examples of how it is used in practice. What can AI help you with?
Transaction Classification
The first example is taken directly from our own case studies: creating an AI-powered transaction classification for a retail bank. In this case, there were main challenges:
- high number of misclassifications occurring in the rule-based logic,
- output data wasn’t self-evident; it required interpretation.
By introducing an ML model integrated with our customers’ mobile banking app and creating an event-driven architecture for delivering real-time categorisation, we were able to lift our customer’s transaction qualification accuracy to 92%, achieving a 98% accuracy rate based on the value of properly qualified customer transactions.
Credit Risk/Score Evaluation
Another common use case of AI for data analytics in the banking industry may be observed in the loan sector. Here, artificial intelligence is often implemented to support risk evaluation and credit score calculation processes. Why is it used?
- It helps you gather multidimensional insights about your potential client by combining data from multiple sources and preparing a more comprehensive calculation.
- It may make the initial credit score evaluation faster.
In this case, the AI system needs to work closely with your employees – it’s important to introduce manual control of the AI-powered evaluations conducted by qualified employees. However, you can support them with the data and insights gathered by artificial intelligence.
Enhancing Wealth Management
AI might also become an invaluable tool for your wealth management team. How do you use it for data analytics in this particular case?
You might, for example, design an AI prompter for your wealth managers. Yet, unlike the traditional prompter used for customer service, this may be filled with information helpful for investment research and personalising portfolio management. As a result, your advisors will create better-tailored offers for their clients, improving their satisfaction and, hence, their loyalty.
Are you looking for reliable software services for wealth management? Then check our financial technology development service!
AI-Driven Document Processing
The best data analytics in banking require accurate, error-free and complete information. However, you often need to input a lot of data manually, especially if it comes from unstructured documents. But what if we told you that this is no longer a problem?
AI-driven document processing is the solution to this issue. With it, you will reduce the number of human errors and extract data more quickly (hence, build insights based on it on time), thus getting a full overview of the information collected by your organisation. Although such systems aren’t designed for data analytics per se, they are a powerful aid to your other solutions, driving data quality to the next level.
Conclusions
AI-powered data analytics and management in banking can truly take your organisation to the next level. To fully embrace this approach, you might need to invest in several different systems and prepare your data. However, the potential benefits are huge, so it is definitely a direction that you should pursue.
You might also read: How to Adopt Artificial Intelligence in Your Business?
