Data management and how to keep our data safe
What happens with our data, especially in the realm of banking? How can we keep our data from being misused? Sébastien Cuny, team manager and Thierry Conter,…
AI is a set of theories and techniques used to create machines that can simulate human intelligence. “Machine learning” is one of these AI techniques and is used to extract knowledge from data. “Deep learning” is a subset of “machine learning” that corresponds to a more complex system and uses neural networks.
AI is very good at detecting correlations between a large number of variables and at solving specific problems. But common sense is a vague and subjective concept: its standards change from one country to the next and it is difficult to model with rules or mathematical formulas. This is why, despite the progress made on AI, understanding common sense remains a real challenge and a remote possibility.
In 2022, we are thinking more about having responsible AI, ethical AI and explainable AI (XAI). With Covid and the massive use of virtual solutions, some of the hot topics include ensuring cybersecurity and using AI with virtual reality.
We are also talking about “low code and no code”, which offer simple interfaces that can be used by experts and non-experts alike, to facilitate the use of AI and its proliferation, and to allow experts to focus on more complex problems.
AI has helped banks improve their statistical models by analysing more data more quickly. There is no question that AI has a strong presence in fintechs, the start-ups of the financial sector. It eases the workload for some of the daily tasks performed by bank staff. For example, AI has enabled banks to use predictive models to anticipate customers’ needs and win their loyalty. With descriptive models, banks are able to better categorise and understand customer profiles and transactions. In addition, AI has been used to create new currencies, such as bitcoin, and to transform the financial sector.
In the future, AI will enable banks to communicate with customers in real time using more complex channels such as audio, video and virtual reality. It will also be used to understand customer communications automatically through natural language processing. Additionally, AI will facilitate compliance with certain regulatory constraints, such as fraud detection, and ensure respect for the environment. AI will also allow for better risk control when managing loans and investments.
1. Data quality: AI relies heavily on the data it receives. If the data are not reliable, then we can’t expect miracles. That’s why the first thing we have to do is ensure data quality.
2. Infrastructure: AI has proven effective thanks to the new knowledge it extracts from large volumes of data through complex processing done relatively quickly. Infrastructure is therefore very important when we’re talking about AI.
3. AI is not a cure-all: we need to be aware that AI cannot solve every problem. Some problems can be solved with much simpler solutions than AI and can yield better results. Other problems are much too complex and are still in the research phase; AI has not yet provided satisfactory solutions for these types of issues.
4. AI should be responsible and ethical.
5. The goal of using AI is not to replace human staff, but rather to help them with their daily tasks and create new work opportunities.
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