AI is transforming business operations, customer service, and risk management; last year, BFSI corporations invested USD 20 billion in AI capabilities. Financial services companies will look to develop strong AI teams in order to stay ahead of the competition as AI continues to revolutionize the sector.
However, it takes a lot of time, money, and experience to build an AI team. To scale AI in financial services businesses, significant adjustments and transformations are required, such as a move from a rigid working environment to an agile and experimental one and from walled work to collaboration.
Daniel Faggella, CEO and Head of Research at Emerj, recently had a conversation with Dr. Lori Cenci, HSBC Unauthorized Trade Surveillance Lead and Lecturer of Applied Analytics at Columbia University School of Professional Studies, to learn more about her experience with best practices for hiring and recruiting for AI adoption projects in the financial services industry.
In the study of their discussion that follows, we look at two important takeaways for creating productive AI teams:
Creating societies centered on analytics: Analyzing organizational structure and creating fluid, agile, and non-siloed teams to integrate data is essential to the success of AI adoption programs.
Including risk in dashboard integration: supplying sufficient background information so that management is not constantly called upon to address data indicating pressing business issues.
Creating Cultures Based On Analytics
Dr. Cenci starts off by explaining to Emerj’s audience how to create a culture that will support the success of AI initiatives and possibly bring value.
She highlights two important ideas for how businesses can design their operations:
To ensure that information, data generation, and analytics flow smoothly, concentrate on keeping activities fluid both laterally and vertically.
Implement agile frameworks in their business processes. Companies must ask insightful questions to ensure that the data is accessible for the other departments inside the company as well as where the job is being done.
Lori adds that because business functions are segregated, many firms sit on top of massive amounts of underused data assets. She recommends that they combine the operational aspect of the firm to become fluid in order to take use of the numerous data sources. Although she acknowledges that it is extremely difficult, she maintains that it is possible.
The way the organizational structure is constructed is a third element that Dr. Cenci says contributes to the expansion of AI in the business. She is adamant that the way business executives define roles, IT teams, and analytics development needs to alter. According to her, an organization with the closest distance between its experts and analytics researchers achieves the best results.
The quality of projects and analytics should be the organization’s fourth priority, according to her. Achieving success requires not only meeting deadlines but also involving the entire team and curating the analysis.
According to Lori, it’s now typical to see people working in silos, but we need more cooperative teams. She cites an instance from her experience where she was told that an analytical dashboard had been created for her by someone on the offshore team, but the black box issues quickly became too much to handle. “What is this analysis trying to solve?” she asked herself.
The experience taught Dr. Cenci that the stakeholders may need to be more technical at times. Technical specialists, on the other hand, might not be the subject matter experts in the particular area that stakeholders require clarity on.
In the past, the two have interacted similarly to requirement gathering and delivery. However, we now require an interactive comprehension of the issue. Dr. Cenci says that open communication is yet another element that contributes to AI’s success within the company.
Last but not least, Lori advises company executives to begin challenging advancements and asking “so what?” about the value they add to the company. Lori advises companies to create diverse teams where the project manager, DevOps, and data scientists feel free to talk about what they are creating in order to create more truly skeptical teams.
Including Risk In Dashboard Integration
Lori goes on to discuss quantifying how dashboards and analysis affect the company. When organizations are seeking to address an issue, we should consider other aspects that fit into the larger picture, not only how much money is saved.
Emerj is informed by Dr. Cenci that she observes a lot of inactive dashboards that may be more beneficial to the company. She believes that because dashboards are now widely accessible, we have forgotten how they were created and that leaders frequently misunderstand why they were created in the first place. She advises the company to consider the decision they are attempting to make and the metrics that support it.
She argues to Daniel Faggella, CEO of Emerj, that company executives should put more effort into integrating data streams inside their companies. She believes that operational areas like surveillance or compliance still have the chance to evaluate risk, but financial services organizations in particular have the means to do so.
According to Dr. Cenci, company executives should consider if the first incident is actually comparable to the others from the perspective of data classification if they observe four instances that indicate increased insider risk.
She advises financial services executives to develop their business plans in conjunction with data collection in order to overcome these obstacles. These frequently require a deeper comprehension of business objectives than simpler, data-driven methods of ROI.
Instead of depending on such observable, “black and white” outcomes, Dr. Cenci advises company executives to concentrate more on risk assessment. She acknowledges that firms are under pressure to disclose operational data, which are frequently the focus of “black and white” approaches, but they need to figure out how to strike a balance.
Companies are moving to a more adaptable environment where monitoring operations involves more than just inputs and outputs; it also involves considering many viewpoints. In order to measure the true KPIs that highlight business processes, leaders must question whether the current quo is the best approach. Then, they may use the answers to speed up systems that achieve real return on investment.