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Although generative AI models are relatively new, this cannot be said about natural language processing (NLP), which dates back even to the 1950s. Machine learning (ML) and later large language models (LLMs) were always crucial for it, and finally, they gained recognition. How do they impact modern banking? What will the future bring? Let’s discuss it now!
Table of Contents
- Machine Learning: Is It Really Innovative?
- Machine Learning & Data in Banking
- Challenges That We Face
- The Takeaway
Machine Learning: Is It Really Innovative?
Artificial intelligence and machine learning have become buzzwords in business. You’ll find them discussed at almost every conference (no matter the industry); they are the talk of the town. But, are they really that new?
Natural language processing started not in 2023 with the release of GPT, not in 2006 with the first public machine translation engines, not even in 2000—it dates back to the 1950s. As such, it has come a long way, and machine learning has been a part of it. So, while we might perceive it as something new, it has been impacting tech for quite a while.
Learn more about the history of NLP and its current uses in banking in our e-book: The New Reality of a Natural Language Processing Engineer
Machine Learning & Data in Banking
Machine learning is helpful wherever there are large quantities of data. Therefore, it has proven to be indispensable in banking.
Take, for instance, analysing transactional data. It’s one of the most valuable sources of customer information, yet it’s vast. The challenge with transactional data analysis is the difficulty in automatically interpreting key attributes like recipient name or description and defining the actual purpose of the customer during the transaction. To leverage the full scope of information in transactional data, incorporating additional attributes is necessary. This is achievable through the application of machine learning models. Simply said, with ML, you can easily extract insights from transactional data and gain a better understanding of your customers, their needs, and preferences and even determine market trends.
Discover the power of transactional data:
However, the above example depicts only a portion of information, one that is structured. What about unstructured data, like that found in documents? This is the place when ML, LLMs, and AI truly excel.
In banking, understanding customer preferences based on past activities and predicting future behaviour requires a comprehensive approach. The response to these challenges lies in machine learning models designed to process sparse and multidimensional customer data and efficiently transform it into context-rich vectors.
Interested in the topic of proper customer data processing with ML models? Read our e-book: Modern Approach to Banking Customer Data Representation!
Challenges That We Face
Although ML, LLMs and AI are undoubtedly a great aid to banking, there are quite a few challenges you need to overcome. Low data quality, data bias, etc. Yet, the most important obstacle here is…degradation.
According to joint research by MIT, Harvard, the University of Monterrey, and Cambridge, 91% of machine learning models degrade over time. This puts you at risk of your system becoming ineffective in the future. However, with proper monitoring and certain measures, you can prevent this. What do you need to do? You’ll find the solutions in our e-book: Ageing of Machine Learning Models in Finance!
The Takeaway
Machine learning is a mighty ally for banks. However, you need to take proper care of your models to ensure they maintain their efficiency and effectiveness. You should also follow the current trends, as this tech, despite not being new, is still evolving. Hence, developing an ML model or an LLM is just the first step—it requires monitoring and upgrades with time.
Are you looking for trends regarding ML? Read our article: Applying graph neural networks for hyper-personalized banking services!
