A shortage of skilled data scientists is stalling banks’ AI ambitions as legacy systems and messy data hold back risk innovation © Edoardo Nicolino/Dreamstime
A shortage of skilled data scientists and engineers is hampering banks’ efforts to modernise their risk infrastructure and unlock the full potential of artificial intelligence, according to senior industry figures.
Financial institutions are sitting on mountains of “disorganised and inaccessible data”, often spread across legacy systems and incompatible platforms, with a lack of in-house expertise emerging as a critical roadblock to innovation.
“There is a dearth of data science experts — the secret sauce we need to manage and harness this data effectively,” wrote Dr Rohit Dhawan, group head of AI at Lloyds Banking Group, in a recent article for The Banker’s sister publication Banking Risk & Regulation.
Data crunch
Recruiters say messy, fragmented systems are holding back many firms. “This is one of the most relevant issues currently exercising risk hiring managers,” says Ian King of search firm Armon-Jones Partners.
“Banks have got tonnes of data, but it’s badly organised and inaccessible — often due to legacy platforms being smashed together with newer systems, with databases held together by string-and-glue front ends.”
Risk managers are increasingly focused on recruiting data expertise, sometimes at the expense of traditional financial risk backgrounds.
“We’ve recently placed two people into market risk teams where the core requirement was experience in data management,” says King.
In one of these hires, King said the job description evolved into a 70:30 split between data science and risk attributes — a reversal of the typical priority.
“We started by looking for a MR manager with data-science programming knowledge, but that person doesn’t really exist yet, and it became clear that [data science] was driving the skillset required for the role.”
“Hire data scientists early and invest in giving them risk experience — even if that means taking on a degree of risk.”
Ian King, Armon-Jones Partners
Bank hiring boards that understand the importance of harnessing big data are prioritising foundational data-engineering skills. However, while such individuals are in high demand, King says not all possess the experience necessary to navigate the complexities of financial risk.
“There are plenty of data scientists around, but they still tend to be relatively young and inexperienced in fields such as financial risk management.
“But based on what I learned from that search, if I were to do it again, I’d go for a [data science] skillset first, and then see what the potential is for them to learn the risk aspects.”
His advice to banks? “Hire data scientists early and invest in giving them risk experience – even if that means taking on a degree of risk in the short term.”
Data minefield
Data engineering – the task of finding and organising data into manageable parcels, using state-of-the-art and accessible database-design thinking. This task can be executed by banks using their IT staff.
Data science – the far more “sexy” data science layer sits on top and is run by experts analysing the (now-accessible) data in enriched ways. Experts currently tend to be hired into financial institutions for these roles from other sectors.
Source: Ian King, Armon-Jones Partners
‘Financial services firms are now talent targets’
Stefanie Coleman, a principal in EY’s people advisory services, agrees that banks urgently need to strengthen their AI talent base. However, she notes that empathetic approaches are still necessary, as some members of the public and specific individuals within banks continue to view the technology with suspicion.Stefanie Coleman
“Apart from core technical skills, banks also need people who can take a human-centric approach to AI — building trust with users and communicating its value.”
Sam Burman, global managing partner of the frontier tech practice at executive recruitment firm Heidrick & Struggles, notes that banks were among the early movers in building AI teams.
“If we think about which sectors have been hiring and building AI/data science talent consistently . . . for many years, banking has been near the top of that list — potentially the top — given the size, complexity, wealth of data and regulated nature of the business.
“What we have noticed more recently is that these large data science teams have become targets for other industries to poach from. So you could argue that in certain banks, their teams have thinned.”
In response, banks have expanded their talent search beyond traditional hiring pools. “Financial services firms traditionally used to hire from other FS firms,” Burman adds. “But now, as long as the data scientist has experience dealing with large data sets and scale — from telcos, utilities, airlines — banks have a broader pool to fish from.”
The World Economic Forum’s annual Future of Jobs report says data scientists and related roles are expected to grow exponentially, while the US Bureau of Labor Statistics projects demand for these positions to increase by 36 per cent between 2023 and 2033.
According to Heidrick & Struggles’ 2024 survey of more than 400 executives from around the world, data science was viewed as the second-most vital area of expertise to build or maintain over the next three to five years.
Uroš Zver, a colleague in Heidrick & Struggles’ Amsterdam office, adds that purpose-driven roles in other industries remain attractive.
“Many top data scientists are drawn to tech, healthcare or climate — sectors where they feel they can move fast and make an impact. Banking, by contrast, can seem slower and more constrained. The irony is that some of the most challenging and consequential data problems today reside within financial institutions.”
Burman adds that large compensation packages in other sectors continue to be a draw. “The Big Tech companies have been and continue to write cheques that many data scientists, who are often very young and motivated by cash, can’t say no to.”
Going down a global, ethical route
Anna Romberg, co-founder of the Nordic Business Ethics Initiative, says: “It is evident that more intelligent use of data within the ethics and compliance programmes is on the rise. Financial firms pick up a lot of ‘red flags’ within their due diligence processes [and] existing data must be translated into actionable information that can be escalated in a meaningful way.
“Regulators are also expecting companies to gather data that is both predictive and detective when it comes to corporate behaviours. There is a high demand for these skills.
“Data scientists may be part of this ‘solution’, but more analysis and reports are not the full answer to dealing with non-compliance within financial institutions.”
Burman says that data scientists are “ultimately there to turn data into actionable insights” and keep commercial leaders abreast with how customers interact with a bank’s suite of products.
In his view, “their input will ultimately span back-end processes to front-end commercial levers”.
“Outside of the quality of their products and rates, the quality of customer experience and their interaction with mobile/online banking is core to customer satisfaction and stickiness,” he adds.
Will data scientists be automated out of a job?
Could AI render data scientists obsolete? Burman says he is seeing large AI and data science organisations “curtail their hiring of data scientists”.
Currently, firms at the cutting edge of automation are “reviewing how much of those roles can be automated or augmented using AI”.
One company trying to bridge this gap is Tel Aviv-based Pecan AI. Its CEO Zohar Bronfman, says he founded the firm to “solve a growing imbalance — demand for predictive models and AI was accelerating, but the supply of proficient data scientists wasn’t keeping pace”.
He explains: “We focused on building deep, AI-based automation to replicate much of the technical work traditionally done by data scientists.”
The company works with a string of financial institutions, including the Brazilian institution Banco BV.
“These organisations use Pecan to apply predictive analytics across key functions — from credit risk modelling to customer retention — without needing to significantly expand their internal data science capacity,” Bronfman says.
He advises banks to rethink their hiring priorities: “Instead of focusing solely on technical PhDs, banks should seek professionals with strong business insight and a solid grasp of data. The ability to define the right questions and translate predictive outputs into business action is now more valuable than deep statistical modelling. It’s about blending domain expertise with data fluency.”
The last word goes to Dhawan, the AI chief who has helped Lloyds Banking Group build an AI Centre of Excellence with 350 experts.
“True innovation and magic happen,” he says, “when skilled colleagues have a safe environment to develop their ideas and access to expertise when needed.”
This article first appeared in Banking Risk & Regulation, a sister publication of The Banker
A shortage of skilled data scientists and engineers is hampering banks’ efforts to modernise their risk infrastructure and unlock the full potential of artificial intelligence, according to senior industry figures.
Financial institutions are sitting on mountains of “disorganised and inaccessible data”, often spread across legacy systems and incompatible platforms, with a lack of in-house expertise emerging as a critical roadblock to innovation.
“There is a dearth of data science experts — the secret sauce we need to manage and harness this data effectively,” wrote Dr Rohit Dhawan, group head of AI at Lloyds Banking Group, in a recent article for The Banker’s sister publication Banking Risk & Regulation.
Data crunch
Recruiters say messy, fragmented systems are holding back many firms. “This is one of the most relevant issues currently exercising risk hiring managers,” says Ian King of search firm Armon-Jones Partners.
“Banks have got tonnes of data, but it’s badly organised and inaccessible — often due to legacy platforms being smashed together with newer systems, with databases held together by string-and-glue front ends.”
Risk managers are increasingly focused on recruiting data expertise, sometimes at the expense of traditional financial risk backgrounds.
“We’ve recently placed two people into market risk teams where the core requirement was experience in data management,” says King.
In one of these hires, King said the job description evolved into a 70:30 split between data science and risk attributes — a reversal of the typical priority.
“We started by looking for a MR manager with data-science programming knowledge, but that person doesn’t really exist yet, and it became clear that [data science] was driving the skillset required for the role.”
“Hire data scientists early and invest in giving them risk experience — even if that means taking on a degree of risk.”
Ian King, Armon-Jones Partners
Bank hiring boards that understand the importance of harnessing big data are prioritising foundational data-engineering skills. However, while such individuals are in high demand, King says not all possess the experience necessary to navigate the complexities of financial risk.
“There are plenty of data scientists around, but they still tend to be relatively young and inexperienced in fields such as financial risk management.
“But based on what I learned from that search, if I were to do it again, I’d go for a [data science] skillset first, and then see what the potential is for them to learn the risk aspects.”
His advice to banks? “Hire data scientists early and invest in giving them risk experience – even if that means taking on a degree of risk in the short term.”
Data minefield
Data engineering – the task of finding and organising data into manageable parcels, using state-of-the-art and accessible database-design thinking. This task can be executed by banks using their IT staff.
Data science – the far more “sexy” data science layer sits on top and is run by experts analysing the (now-accessible) data in enriched ways. Experts currently tend to be hired into financial institutions for these roles from other sectors.
Source: Ian King, Armon-Jones Partners
‘Financial services firms are now talent targets’
Stefanie Coleman, a principal in EY’s people advisory services, agrees that banks urgently need to strengthen their AI talent base. However, she notes that empathetic approaches are still necessary, as some members of the public and specific individuals within banks continue to view the technology with suspicion.Stefanie Coleman
“Apart from core technical skills, banks also need people who can take a human-centric approach to AI — building trust with users and communicating its value.”
Sam Burman, global managing partner of the frontier tech practice at executive recruitment firm Heidrick & Struggles, notes that banks were among the early movers in building AI teams.
“If we think about which sectors have been hiring and building AI/data science talent consistently . . . for many years, banking has been near the top of that list — potentially the top — given the size, complexity, wealth of data and regulated nature of the business.
“What we have noticed more recently is that these large data science teams have become targets for other industries to poach from. So you could argue that in certain banks, their teams have thinned.”
In response, banks have expanded their talent search beyond traditional hiring pools. “Financial services firms traditionally used to hire from other FS firms,” Burman adds. “But now, as long as the data scientist has experience dealing with large data sets and scale — from telcos, utilities, airlines — banks have a broader pool to fish from.”
The World Economic Forum’s annual Future of Jobs report says data scientists and related roles are expected to grow exponentially, while the US Bureau of Labor Statistics projects demand for these positions to increase by 36 per cent between 2023 and 2033.
According to Heidrick & Struggles’ 2024 survey of more than 400 executives from around the world, data science was viewed as the second-most vital area of expertise to build or maintain over the next three to five years.
Uroš Zver, a colleague in Heidrick & Struggles’ Amsterdam office, adds that purpose-driven roles in other industries remain attractive.
“Many top data scientists are drawn to tech, healthcare or climate — sectors where they feel they can move fast and make an impact. Banking, by contrast, can seem slower and more constrained. The irony is that some of the most challenging and consequential data problems today reside within financial institutions.”
Burman adds that large compensation packages in other sectors continue to be a draw. “The Big Tech companies have been and continue to write cheques that many data scientists, who are often very young and motivated by cash, can’t say no to.”
Going down a global, ethical route
Anna Romberg, co-founder of the Nordic Business Ethics Initiative, says: “It is evident that more intelligent use of data within the ethics and compliance programmes is on the rise. Financial firms pick up a lot of ‘red flags’ within their due diligence processes [and] existing data must be translated into actionable information that can be escalated in a meaningful way.
“Regulators are also expecting companies to gather data that is both predictive and detective when it comes to corporate behaviours. There is a high demand for these skills.
“Data scientists may be part of this ‘solution’, but more analysis and reports are not the full answer to dealing with non-compliance within financial institutions.”
Burman says that data scientists are “ultimately there to turn data into actionable insights” and keep commercial leaders abreast with how customers interact with a bank’s suite of products.
In his view, “their input will ultimately span back-end processes to front-end commercial levers”.
“Outside of the quality of their products and rates, the quality of customer experience and their interaction with mobile/online banking is core to customer satisfaction and stickiness,” he adds.
Will data scientists be automated out of a job?
Could AI render data scientists obsolete? Burman says he is seeing large AI and data science organisations “curtail their hiring of data scientists”.
Currently, firms at the cutting edge of automation are “reviewing how much of those roles can be automated or augmented using AI”.
One company trying to bridge this gap is Tel Aviv-based Pecan AI. Its CEO Zohar Bronfman, says he founded the firm to “solve a growing imbalance — demand for predictive models and AI was accelerating, but the supply of proficient data scientists wasn’t keeping pace”.
He explains: “We focused on building deep, AI-based automation to replicate much of the technical work traditionally done by data scientists.”
The company works with a string of financial institutions, including the Brazilian institution Banco BV.
“These organisations use Pecan to apply predictive analytics across key functions — from credit risk modelling to customer retention — without needing to significantly expand their internal data science capacity,” Bronfman says.
He advises banks to rethink their hiring priorities: “Instead of focusing solely on technical PhDs, banks should seek professionals with strong business insight and a solid grasp of data. The ability to define the right questions and translate predictive outputs into business action is now more valuable than deep statistical modelling. It’s about blending domain expertise with data fluency.”
The last word goes to Dhawan, the AI chief who has helped Lloyds Banking Group build an AI Centre of Excellence with 350 experts.
“True innovation and magic happen,” he says, “when skilled colleagues have a safe environment to develop their ideas and access to expertise when needed.”
This article first appeared in Banking Risk & Regulation, a sister publication of The Banker
#ResearchDataExcellence #DataAnalysisAwards #InternationalDataAwards #ResearchDataAwards #DataExcellence #ResearchData #DataAnalysis #DataAwards #GlobalDataExcellence #DataInnovationAwards #DataResearch #ExcellenceInData #DataAwardWinners#DataAnalysisExcellence #ResearchDataInsights #GlobalResearchAwards #DataExcellenceAwards #ExcellenceInResearchData #ResearchDataLeadership #DataResearchExcellence #AwardWinningData #InternationalResearchAwards #DataAnalysisInnovation #ResearchDataAchievement #ExcellenceInDataAnalysis #GlobalDataInsights #ResearchDataSuccess #DataAwards2024
Website: International Research Data Analysis Excellence Awards
Visit Our Website : researchdataanalysis.com
Nomination Link : researchdataanalysis.com/award-nomination
Registration Link : researchdataanalysis.com/award-registration
member link : researchdataanalysis.com/conference-abstract-submission
Awards-Winners : researchdataanalysis.com/awards-winners
Contact us : rdat@researchdataanalysis.com
Get Connected Here:
==================
Facebook : www.facebook.com/profile.php?id=61550609841317
Twitter : twitter.com/Dataanalys57236
Pinterest : in.pinterest.com/dataanalysisconference
Blog : dataanalysisconference.blogspot.com
Instagram : www.instagram.com/eleen_marissa
No comments:
Post a Comment