Which IT solutions do customers in the financial industry choose most often?
Maciej Kasprzak, General Manager Financial Technology Services: The answer depends on the client. If we look at large banks, they often want technology services that complement each other. In contrast, more innovative areas focus on purchasing ready-to-use digital banking products or components. They do this because they are still at the technology or market research stage, looking for a solution that could address their needs.
Smaller organisations are willing to buy ready-made solutions because they do not want to spend months or even years building complex tools, yet they want to catch up. So they turn to companies like ours and look for ready-made components or specific competencies. So a technology vendor’s history of cooperation with market leaders is very significant to the smaller organisations, as they try to reduce the technological and business gap between them.
Which of the IT technologies used in Ailleron would you describe as innovative?
A good example is AI Banking and the area related to data analytics, i.e., data platforms. These systems collect data from various sources in real-time and make inferences based on the collected information using advanced algorithms or machine learning models.
Artificial intelligence was an empty slogan, not followed by practical application in business for a long time. Occasionally, companies use machine learning and deep learning in narrow specializations. For example, in the financial sector, very innovative units and IT departments have experimented with this technology, e.g., in fraud detection or risk management. At Ailleron, in the area of AI, we are focusing on ML (machine learning), NLP (natural language processing) and DL (deep learning) technologies.
We use machine learning (with deep learning and neural networks), among others, to create advanced transaction classifiers or recommendation models to capture valuable information from the mass of data that human analysts cannot perceive.
In terms of business, ML allows you to deliver complex data analysis results faster. In terms of technology, it will enable us to move away from coding certain functionalities and tedious creation or maintenance of business rules in favour of self-learning mechanisms.
We use NLP (natural language processing) to optimise chatbot customer service scenarios. We are not a chatbot platforms technology provider, but we provide services to improve conversational scenarios already implemented in the financial industry.
For example, we have implemented PoC (proof-of-concept) for handling credit applications, where the chatbot can conduct a multithreaded discussion with the customer and answer complex questions.
The AI area also includes analytics, predicting the behaviour of end customers of a bank, insurance, or leasing company.
Data analytics requires an optimal Data Platform to integrate data from various systems, ensure quality and consistency, and operate effectively. The Data Platform itself can be used by financial institutions not just for analytics, but it can also be the source of real-time data for systems such as online banking, mobile and CRM. Without burdening the source systems.
The main idea behind AI Banking is making it possible to take immediate, defined, real-time action each time customers performs a specific activity in the bank’s digital channels. Advanced customer segmentation allows us to predict their behaviour, such as the intention to end a relationship with the bank. It enables the institution to take adequate measures to prevent it.
We provide banks with models for classifying customer groups based on historical data or real-time events. Large banks already use similar technologies to a greater or lesser extent. However, in many financial institutions, customer segmentation is still based on parameters set by analysts.
In this case, ML means we can analyze the full spectrum of data and capture the relations or connections unattainable to the human mind. We are also working on using graph databases to conduct multidimensional analyses (Graph ML).
Are Ailleron customers already using cloud products and services?
All our solutions use a mature cloud-native approach. Applications are prepared to run on various cloud platforms from Google, Microsoft, AWS and other vendors. However, we adapt to our client’s preferences in the “clouding” field as financial sector regulations often influence them.
You have extensive experience in working with various retail and corporate banking clients. What banking trends do you find the most popular?
Apart from AI usage, another area of innovation is microservices architecture. All the solutions we create are based on it, which entails high scalability and great flexibility in launching them in various environments. The microservice architecture is already popular in smaller organizations where system replacement processes are relatively simple. Ailleron’s AI Banking solution is designed using an event-driven architecture. Therefore, it doesn’t have to face the challenge of real-time response to events that most existing IT systems must face.
What else? Advanced gamification has already reached banking, and we can see it, especially in banking offers for the youngest generation. Authors of these solutions try to increase users’ mobile app attachment and build a unique customer experience.
The gamification trend will also be driven by machine learning, enabling personalisation for a specific user. It will be an example of moving away from applying typical business rules to understand user habits better and provide hyper-personalised communication.
In conclusion, the market is facing further process automation plus advanced data analytics to draw valuable conclusions from the data.
Before this happens, the financial sector faces investments to refresh and rebuild technology stacks and build scalable, efficient data platforms. These are essential to put in place before entering the world of personalized communication. At Ailleron, we are pleased to support financial institutions in all of these areas.