The “war for talent” in financial services has evolved to encompass new
frontiers and unfamiliar battlefields. This evolution is fueled by the fundamental
transformation of capabilities essential to payments organizations and more
broadly, banks’ future success: skills in digital technology, artificial intelligence,
and automation alongside less tangible abilities such as problem-solving,
emotional intelligence, resilience, and adaptability. Similar transformations are
playing out across sectors however, leaving such capabilities in scarce supply.
At the same time, banks’ historical playbook for attracting, developing, and
retaining talent is in need of update given erosion in the perceived advantages
of a banking career relative to other sectors.
Successfully addressing this challenge pays
dividends. Banks that prevail in the renewed
war for talent will place greater emphasis on
employee attraction, management, development, and retention, starting with revamping
their employee value proposition and embracing
evidence-based talent management
by deploying new capabilities such as data,
analytics, and organizational science. We
believe that banks and payments companies
that recognize their talent management opportunities,
set bold aspirations, and embrace
new capabilities to address these imperatives
will increasingly distance themselves from
their competition.
The new war for talent
The renewed war for talent is global, urgent
and poses daunting challenges, for which the
imperative is not only to attract and retain
the highest performers, but also to enable
leaders to better manage talent to deliver
sustainable competitive advantage. A 2018
McKinsey analysis of European financial services
firms on the future of work identified
the critical talent segments these institutions
need to fill over the coming years. Top of this
list are new roles in software and application
development (e.g., scrum master), analytics
(e.g., data scientists), new risk management
roles (e.g., cybersecurity analysts), and digital
marketing (e.g., UX designer). However,
research from technology-sourcing firm
Catalant finds that while many companies
have begun to address top technology and
training challenges, most continue to rely on
traditional recruiting models that show signs
of erosion, leading to key roles often taking
90 or more days to fill. By 2021, McKinsey
projects that demand for talent with digital
capabilities will outstrip supply by a factor of
four in areas like agile, and by 50 to 60 percent
for big-data talent, according to a study
conducted two years ago.
McKinsey framed the war for talent as a
strategic business challenge in 1997, setting
forth the notion that better talent leads to
better corporate performance. Bank leaders
embraced the concept of talent as their firm’s
most valuable asset; however, responsibility
for hiring and development continued to be
delegated to human resources or line managers.
C-suite leaders focused on other priorities
while battles for talent were won with
monetary incentive packages that tech firms and companies in other sectors were unable
to match.
In the years following the financial crisis,
banks focused on cost reduction and risk
management to battle margin and regulatory
pressures. This left a blind spot for strategic
talent questions, and many banks now
find themselves with a significant gap in
their perceived employee value proposition
(EVP) compared to technology and other
leading sectors (Exhibits 1, 2). As a result, future
leadership talent is turning away from
careers in finance. In 2007, four times as
many US MBA graduates chose to enter the
finance field over technology. By 2017, these
two groups were roughly at parity. IPOs and
similar equity incentives made the tech field
more lucrative, shifting the playing field just
as banks trimmed their post-financial crisis
bonuses. What once was a bank EVP selling
point has now faded in comparison to other
industries.
Exhibit 1
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It’s not only rigorous technical skills that are
gaining importance for banks. An ironic aspect
of the shift towards automation of many
20th-century jobs is the increasing focus
on people skills—flexibility, problem-solving
under uncertainty, collaboration, and
emotional intelligence, to name a few. While
these soft skills often get short shrift when
sized up against measurable technical skills
in Python and deep learning, their value should not be underestimated. A University
of Michigan study showed that investing in
training of soft skills yielded a whopping
250 percent return on investment in certain
instances.
Exhibit 2
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We have reason to believe that the war for
talent is here to stay. Several parallel forces
have fundamentally altered the global landscape,
altering banks’ roadmap to victory in
the talent management space:
Massive amounts of workforce data:
The explosion of data over recent years—
combined with the power to store and
dissect it—opens new avenues for talent
management. Email and calendar data
are now being used to benchmark the collaborative
approaches of effective teams.
Engagement surveys—with greater depth
and quicker turnaround than in the past—offer valuable insights into the themes
and sentiments diverting employee focus,
and employee location data can improve
operational efficiencies in sectors such as
restaurants and delivery services.
Imperatives for speed and accuracy: In
an uncertain competitive environment
marked by ever-shorter technology lifespans,
winners in the war for talent will
be able to quickly identify talent gaps at
a micro-granular level. Agile talent management
will drastically reduce the cost
associated with having the wrong or even
no talent at all in critical roles.
Shortage of talent and a rapidly evolving
workforce: Forty-six percent of employers
have difficulty filling jobs, mainly due to a
shortage of applicants. Finance staff, a core
constituency of banks and payments firms,
rose to the sixth-most-difficult job category
to fill in 2016, up from ninth in 2015.
Winners will be able to fill jobs quicker
and with better people as microtargeting
and evidence-based selection allow firms
to identify and tailor hiring to target talent
and assess candidates more consistently to
hire high performers.
Many banks have taken note of how talent is
impacted by these trends. An analysis of earnings
call transcripts of the ten largest US banks
reveals that talent-related terms are being used
three to four times as often in recent quarters
than they were in the 2012-15 period. With employee
turnover rates at ten-year highs, CEOs
need to find ways to not only secure talent for
the future, but also to stem near-term attrition
and its drag on profitability.
Winning with analytics
We have documented substantial performance
differences between the leaders and
laggards in this new war for talent. Research
outlined in the book Talent Wins, co-authored
by outgoing McKinsey Managing
Partner Dominic Barton, demonstrates
that companies that use data and advanced
analytics to inform their talent decisions
realize up to a 30 percent increase in profits
through hiring focus alone—before accounting
for the benefits of higher productivity
and better retention. Furthermore, 2018
McKinsey research on performance management
indicates that organizations with
effective performance management are 77
percent more likely to outperform competitors
and peers.
Winners will be those firms who can harness
data, advanced analytics, and behavioral science
to make sound people and organization
decisions faster, better, and with a level of
specificity previously unavailable. This will
enable them to preserve advantages gained by
better deploying and nurturing skills across
the full talent lifecycle.
We see three areas in which firms must master
data and analytics in order to win the war
for talent:
Assess talent gaps and address accordingly:
The adage “culture eats strategy
for breakfast” may soon be replaced—or at
least complemented with—“and capabilities
take lunch.” While many organizations
invest significantly in strategy, the key is
securing the capabilities needed to deliver
that strategy. Leveraging internal and
third-party data allow firms to quantify
organizational skills deficits, target opportunities
for re-skilling (through methods
such as hierarchical clustering or cosine
similarity), and identify the skills to source
externally. A 2017 McKinsey Global Institute
report on automation, employment, and productivity showed that 43 percent
of all finance and insurance activities can
be automated through currently available
technology. The aforementioned
McKinsey study on the future of work in
financial institutions found that one-third
of existing talent gaps can be addressed by
re-skilling current employees. One client
established a best-practice adult-learning
program, combining both in-house and
external learning, and retrained more
than 1,000 employees into new internet
of things, analytics, and machine learning
roles within the first ten months of the
program. Winners in automation transformations pinpoint capability requirements
and make the proper call on where to buy
(source), build (re-skill), and rent (outsource
or shift to contractors).
Attracting and retaining the best talent:
Microtargeting allows a firm to tailor its
EVP and its communication to critical
talent segments to increase conversion
rates. There is clear evidence that objective
hiring powered by analytics and
behavioral science (versus traditional
interviews) leads to better hiring decisions
and greater value creation (Exhibit
3). In this area banks can learn from each
other, as well as from other sectors. For
instance, firms like Aegis Worldwide conduct
text message-based initial interviews
to reduce bias and enable algorithmic
analysis of answers. Unilever uses technology
across its whole recruiting process;
it begins by using LinkedIn profiles
instead of résumés, deploying AI to select
the best prospects. Next, it uses a series of
online games to further narrow the field
to a select few candidates for in-person
interviews. The results are convincing:
Using this approach Unilever tripled the
roster of universities from which it recruits
while reducing its average hiring
cycle from four months to four weeks.
McKinsey’s own use of artificial intelligence
to screen résumés not only delivered
30 to 50 percent increases in hiring
efficiency (reflecting a 400 to 500 percent ROI), but also drove an increase in the
share of women candidates passing initial
screening.
Exhibit 3
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One financial services firm found it could
increase field effectiveness by systematically
testing and recruiting for character traits
such as curiosity and de-emphasizing humility,
which was found to be underrepresented
among its high performers. Another applied a
predictive modeling technique called elastic
net regularization to hire more employees
with character traits similar to those of existing
high performers, reallocating recruiting
dollars to particular schools and majors and
disintermediating headhunters in approaching
high-potential candidates. A fast-food
chain collected data on employees’ character
traits and behaviors in the workplace and
generated more targeted recruiting, roles and
expectations, culture-focused trainings, updated
financial and non-financial incentives,
and optimized shifts. The company soon documented
customer satisfaction gains of 130
percent across stores, 30-second speed-of-service
improvements, and per-store revenue
increases of 5 percent.
We predict that winners will go beyond
deploying “off the shelf” assessments to
develop evidence-based models supporting
the knowledge, skills, attributes, and experiences
required to successfully deliver on
a specific role in its unique environment.
This can be accomplished through closed loop
machine learning to pinpoint what
factors distinguish high performers from
the rest, or science-based forensics on future
work required, which informs objective
screening criteria to be assessed through science-backed interviews and digital assessments,
gamified or otherwise.
High performers are often at the highest risk
of attrition, given their multitude of outside
options. Data and analytics can serve as
an early warning and mitigation system by
predicting attrition risk at both individual
and group levels and developing effective
responses to address the root cause. For instance,
using k-medoid and majority vote classification
techniques, one financial institution
found that attrition was elevated among three
different groups—millennials seeking professional
growth, employees working in larger
teams, and those working for low-tenured
managers. Leveraging this data-driven employee
segmentation, the organization developed
tailored preventive measures to reduce
attrition for each of the clusters.
Better manage and deploy talent
A plan to grow and deploy talent starts with
identification of what drives true performance—collecting data to create a 360-degree
view of who your employees are, what
they do, who they interact with, how they’re
deployed—linking this information to the
relevant dependent variables and building
optimization strategies. This typically starts
with a data-driven assessment of the organizational
context for employee performance. For
instance, to what extent does a manager’s span
of control impact individual performance?
What role does coaching play in performance?
More ambitious initiatives might develop
guidance on time allocation, collaboration patterns,
meeting practices, and more, through
behavioral data such as calendar and email
metadata (with appropriate encryption methodologies
to maintain employee privacy).
As an example, one financial institution
built and analyzed a behavioral dataset of
how leaders split their time across recruiting, coaching, clients, and other activities to
identify a 30 percent growth opportunity in
investments. The bank reallocated leaders’
time from administrative and controls-oriented
tasks to customer-centric coaching
and fostering connectivity across lines of
business. Another firm leveraged McKinsey’s
data-driven Talent to Value approach to identify
a select number of critical roles driving
the most economic value—some extending as
far as four levels below the CEO. This client
discovered that closer collaboration across
four of 30 critical roles was critical to delivering
more than 50 percent of the value at
stake. Rather than merely encouraging general
collaboration across the enterprise, the
firm doubled down on tactical incentives for
collaboration among these roles by, for example,
implementing shared goals, with 30 to
50 percent of leaders’ KPIs driven by factors
beyond their direct control.
These steps create a “virtuous cycle” benefitting
workers as well as employers. A
better selection process leads to better
organizational fits, which in turn fosters
employee satisfaction and enhanced EVP.
When organizations are redesigned to be
more collaborative and agile, not only does
employee time allocation change, but roles
evolve too and traditional management hierarchies
become redundant. At the end of the
process, one European bank eliminated two
entire layers of middle management while
its employee engagement scores rose by over
20 percentage points.
What can be done today?
Humans do not change their behavior with
the flip of a switch. It may take years to get a
single individual to change behavior, which
is only compounded when we consider the
thousands of employees with unique values,
goals, and aspirations working at modern-day
organizations.
While many people-related changes take
time to reach full potency, most organizations
possess the building blocks in both
capabilities and data to start with small
changes today to pave the way for larger
shifts tomorrow. A key first step is to identify
the human component of business challenges
and opportunities, and build an analytics engine
to collect data and validate hypotheses
on performance drivers. For example, in a
corporate bank, analytics on calendar metadata may help pinpoint the interaction patterns
related to deal success. While in payments,
data and analytics can enable faster
and more nuanced hiring of the right combination
of technical and “soft” skills. Deploying
analytics to create transparency into
what matters—for leaders, managers, and
employees—empowers them to cut through
the noise and focus on what really matters.
Financial institutions looking to upgrade
their talent management practices can follow
a few simple guidelines to get started:
Make talent the business’s agenda: A
firm’s people analytics agenda must focus
on critical business needs and originate
from a strong hypothesis on which factors
do and do not matter to business performance.
Setting this agenda is a collaboration
between business and HR leaders.
Don’t underestimate what you already
have: Relevant data is often already available
and can be complemented with nominal
effort. In our experience, three out of
four banks already possess the necessary
data—such as attrition rates, team structures,
employee backgrounds, and average
time to fill a position—to test the most pressing people analytics hypotheses.
Treat data with the care and rigor it
deserves: Protecting data privacy and
employee confidentiality are critical objectives,
not only since the GDPR rules
on data protection took effect earlier this
year. Protecting data privacy is also core
to preventing a situation where employees
feel they are being unduly monitored or
even manipulated. Create transparency
on which data is sourced, how it is used
and the tangible benefit that people analytics
can provide. Establish protocols and
encryption policies to appropriately anonymize
and mask information.
Start small and build over time: Significant
value can be gained by combining HR,
financial, and operational data for basic
“talent due diligence.” The first step is to
identify drivers of compensation growth,
performance ratings, promotions, and
variance across the organization. This will
begin to infuse talent decisions with the
rigor normally reserved for financial decisions.
By running a talent due diligence,
one European bank quickly identified an
opportunity for retooling, finding that
most new employees were not being hired
into the most critical divisions and roles,
and that base pay was more correlated with
age and job grade than criticality of the
role or performance. The people analytics
journey is a transformation, comparable
to robotic process automation. Start small,
adhere to high standards when handling
data, and quickly prove the value of the approach.
A test-and-learn approach makes a
difference, running trials to prove business
value before scaling more broadly.
Twenty years ago, the war for talent was
fought with major changes in employee
environment and compensation systems,
triggering a number of innovations and a new
informality—down to casual Fridays. Today’s
changes are more nuanced and targeted. Instead
of large-scale changes, the new war for
talent will likely involve thousands of subtler
microdecisions. This scope can seem daunting.
Fortunately, embedding data and analytics
into an organization’s people function
begins with a few simple changes today that
will lay the groundwork for a more profitable
organization and more fulfilled employees.