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Most banks today are at the beginning of their AI journey, and they fundamentally have a lot of the pieces in place already - but moving forward isn't easy.
This white paper breaks down:
Jason has more than 20 years of experience in financial services and technology. After beginning his career as an equity sales trader with Deutsche Bank, Jason continued on as a Junior Portfolio Manager for a boutique asset management firm. He subsequently spent the majority of a nearly nine-year career at Bloomberg in the electronic trading group. He currently leads financial services sales across Eastern United States and Canada at Dataiku.
Pierre has 14 years of experience in technology, mainly dealing with financial services customers. He started his career at a French IT consulting firm in its dedicated Banking & Insurance unit. Today, Pierre leads the financial services team in France at Dataiku. He has been at Dataiku for more than five years, which means he worked with its very first customers and has been able to see the expansion and deployment of more advanced use cases over time.
Starting his career off in consulting focusing on financial services, Hursh spent most of his time working with investment management companies. At M&G Investments (Prudential), he worked closely with the company’s Analytics Centre of Excellence as well as projects within risk, finance (AUM and FUM analysis), and operations. Today, he leads financial services sales in the United Kingdom and Ireland at Dataiku.
This 40-page white paper takes a deep dive into:
Ultimately, AI for banks means turning data from the cost center it is today in to a revenue stream - a source of efficiency and a wealth of information that can be used to provide fundamental value to the business. This white paper aims to put financial institutions on the path to realizing that potential.
In a topic intrinsically intertwined with regulatory requirements, the struggle between velocity and proper processes for model risk validation can be debilitating for the progress of AI initiatives. Of course, the challenge is a complex one because mitigating risk is the top priority, which means that it cannot be sped up in a way that compromises the quality of the validation itself.
But there are still improvements that can be made and ways to address this challenge; namely by introducing consistency and reproducibility into the process both of validations and the final pushes to production.
For example, it is often the case that the model risk validation team(s) look at models from different organizations or groups across the company, each of whom have their own individual processes and send the models in different formats, containing different information, etc.
That means for each review, the model risk validation team loses time in trying to get their bearings and figure out what it is that they’re looking at. Similarly, without a consistent system or process by which models are delivered, the next step (deployment to production) also becomes complicated and time consuming.
New technology gives banks the power to collect, store, and analyze exponentially more information than was imaginable not too long ago. Yet most struggle amidst complexities of the data itself, regulations, and more, to get AI initiatives off the ground. But they don’t have to.