AI & Data Science Manager at Deloitte Talks About AI Applications and Challenges in Banking

What are some struggles financial institutions are facing?


Wow AI

a year ago | 2 min read

Photo by Wow AI

*Disclaimer: Wow AI has received approval from Mr. Beydia to distribute the interview across platforms.

With the maturation of AI in banking and the increasing acceptance of AI solutions, higher-complexity solutions with favorable return-on-invest (ROI) across business areas are potentially possible.

According to an OpenText survey, 80% of banks are well aware of the potential advantages offered by AI and machine learning. Banks can use AI to transform the customer experience by enabling frictionless, 24/7 customer service interactions.

But the use of AI in banking applications is not simply for consumer financial services.

We will now learn more about AI in banking from Mohamed Beydia, an AI and Data Science Manager at the world’s largest accounting firm, Deloitte.

You can watch the whole interview here.

About the speaker

In a short interview with Wow AI, Mr. Beydia shared with us firstly how he got to where he is right now. After receiving a Master’s degree in Statistics and Econometrics, he worked in Crédit Agricole as a data scientist and worked his way up to become Head of Data Analytics and Digital Marketing. Then, he spent a couple of months at PwC Luxembourg before joining Deloitte as an AI and Data Science manager.

We are honored to have him as a guest speaker at the Worldwide AI Webinar this September 29–30, where he will discuss the latest use cases of AI in banking with an emphasis on why the banking sector is behind other sectors in terms of the use of machine learning.

Struggles of the banking sector with AI

Mentioning the struggles that banks are facing in the process of embracing the full potential of AI/ML and how to overcome them, Mr. Beydia believed data quality is key and stems from strong data management and governance. Having high-quality data, comprehensive data processes, complete business glossaries, and understanding the business meaning behind every data point are a few steps banks need to take.

“The main problem is most of the traditional banks don’t trust the quality of the data they have. And there is no real data governance because we don’t know who owns the data. We don’t know who can give us the business meaning of the data and data scientist teams feel alone. So that’s one of the key issues that banks need to solve first to fully exploit the potential of AI.” — Mohamed Beydia, AI and Data Science Manager at Deloitte

Another problem that banks are dealing with is compliance and regulatory restriction. Mohamed shared that many financial institutions are struggling with incorporating AI at scale into their businesses due to potential risk exposure when it comes to handling customer data. In fact, certain regulation policies like GDPR in Europe requires that financial institution must obtain consent from customers to collect data and derive insight from it.

Mr. Beydia has also witnessed over the years how reluctant industry people are towards AI/ML. They deem it too complex, unpredictable, and uncertain. ROI is not ensured and the amount of investment is high.

To deal with this, he recommended showing executives and decision-makers that AI/ML is not sophisticated. In fact, through ‘quick-wins’ use cases they can generate value quickly. A prime example is fraud detection. By presenting to the skeptic that an AI model, including its installing timeline and added values, has the ability to save banks lots of money, one can convince executives to adopt AI.

This is just a fraction of what Mohamed Beydia will discuss at the Worldwide AI Webinar.

To directly learn more from him and engage in meaningful conversations with our keynote speakers, save your spot here:

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