AI Deployment in Financial Services

Scaling AI Sophie Dionnet

In the NewVantage Partners Big Data and AI Executive Survey 2021, “financial services firms continue to be at the forefront in terms of the scope and depth of their data requirements and initiatives.” In this domain, the verbiage tossed around by practitioners is all about untapping efficiency across the data science life cycle so that, ultimately, the team is able to produce more. In this post, we’ll summarize some of the key challenges faced by financial institutions, ways to overcome these strategies, and some popular AI use cases in the financial services industry.

Sophie Dionnet

What Are the Key Challenges?

According to Dataiku’s General Manager — Business Solutions, Sophie Dionnet, financial institutions face a myriad of challenges when it comes to using data to make decisions at scale:

  • Data access and quality: Many are up against data silos from a historical “product-only approach to data, fragmented information systems from previous mergers and acquisitions, and a lack of a cohesive, organization-wide data culture.
  • Alignment for regulatory compliance: Banks and insurance companies, for example, can be reluctant to broaden access to data due to things like regulation, perceived risks on infrastructure resilience, and more. While it’s a unique challenge indeed, there are ways teams can work with data within the confines of a well-defined governance framework.
  • Upskilling and trust: Given the deep connection to data quality and modeling, financial organizations are hesitant to embrace machine learning techniques and fully embed them in financial processes, notably due to model risk management and explainability requirements.

How Can We Overcome Them?

Each challenge mentioned above can be solved with the right Enterprise AI strategy and an inclusive mindset.

  • Data democratization: Scaling with analytics starts with broadening access to data. Technology will support end-to-end data perspectives, and building a strong data culture with distributed ownership on quality is also essential to break the “user-only” disempowered approach, supported by strong data governance programs.
  • Data governance: There is a need for strong enablement and governance programs to educate staff on the rights and wrongs of using data and build referential analytics. In the age of AI, data governance strategies need to be established in a way that successfully handles the massive shift toward democratized machine learning.
  • Collaboration: Companies willing to embrace analytics have to build a suitable collaboration environment to organize exchange and controls between risk experts, business professionals and data scientists to develop well-controlled initiatives. Along with data democratization efforts, such methods will help teams be well equipped to work together and tackle larger-scale projects. Collaboration brings together different skills and expertise to best enrich the company as a whole by making it become more data driven and efficient.

new york city sky view

Some Common Use Cases

Sophie Dionnet highlights two notable categories of use cases applicable by financial institutions:

  • Customer analytics and customer journey enhancement: Financial services are confronted with an aggressive competitive landscape and customers are demanding more personalization in their experiences. Thus, this trend has improved customer orientation in such organizations. The capacity to build 360-degree customer views and optimize customer journeys, notably on claims management, are two examples of areas where AI has significantly supported deep transformation within banks and insurance companies.
  • Risk management across all dimensions: The successful integration of AI in risk management has played an essential role in supporting reinforced robustness of the banking system, including agility and impact in investigations, development of new internal controls, and enhancement of financial crime monitoring through analytics. AI has also revolutionized risk assessment, notably through the enhanced use of alternative data. For both traditional risks and emerging risks such as climate change, such progress has helped all financial players — banks and insurers alike — reconsider how to price risks.

    These are only some of the many benefits of AI in the financial sector.

    Dataiku responds to the challenges financial institutions face in scaling up AI efforts and is already working with major financial institutions worldwide.

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