The global banking industry continues to endure one of
the most difficult times in its history. Laurence Trigwell, global
financial services executive at IBM subsidiary Business Analytics,
argues that business analytics software has a key role to play in
meeting the challenges of operating a retail bank in the
21st century

 

With the effects of the financial crash of 2008 still being
keenly felt, the global banking industry is going through one of
the most challenging periods in its history. For many retail banks,
there is a pressing need to examine and adjust the way in which
they run their businesses or risk being sidelined by leaner and
smarter competitors.

As such, I believe that business analytics software – which can
help companies to gain rapid insight from their data and make more
informed decisions – has a key role to play in meeting the
challenges of operating a retail bank in the 21st
century.

If we take a closer look at the current financial services
environment, it’s clear that running a bank in a ‘business as
usual’ fashion is no longer a viable option. Capital is scarce,
increasingly expensive and likely to remain so for the foreseeable
future. Customers understand their importance and are prepared to
shop around for the best terms making banks work harder to
customise offers, for the right customers and segments.

Finally, of course, a new, prescriptive regulatory environment
has emerged, demanding more frequent and detailed disclosure with
increased capital requirements. Profitability is severely impacted
and requires new business models to be developed. 

 

Sustainable competitive advantage

Creating analytic insight from the wealth of information
available to us and using that insight to inform strategy, create
new business models, anticipate outcomes and align decision making
creates sustainable competitive advantage. IBM

Additionally banks face a problem that’s common to all large,
customer-facing organisations: the explosion of ‘big data’.

The growth of electronic and online media means that companies
are constantly bombarded with information that more often than not
they have trouble storing properly, let alone managing and
analysing.

It’s also worth considering that over 80% of all information
stored by the average company is in an unstructured, text-based
format – word documents, emails, presentations etc – which
traditional data analysis tools are unable to access.

There is an amazing amount of information and insight captured
in these unstructured documents and conversations.

Banks can learn from their customers about product quality,
customer experience, price, value, service and more, while internal
conversations may contain valuable nuggets relating to strategy,
projects, issues, risks and business outcomes. The challenge is
sifting the gold from the silt.

This is where business analytics software comes in, with its
potential to help banks increase customer profitability and
satisfaction, manage risk and be more operationally efficient.

By bringing together all relevant information (structured and
unstructured, internal and external), banks can answer fundamental
questions such as ‘What is happening?’, ‘Why is it happening?’,
‘What is likely to happen in the future?’, and ‘How should we plan
for that future?’.

Business analytics software does this by unlocking the value of
data captured in operational and financial systems, transforming it
into useful, relevant information which creates a clearer picture
of what’s behind critical issues, trends and opportunities. In this
way, banks can gain an accurate forward-looking view of their
business and plan accordingly.

Banks have always been high on the maturity curve for employing
business analytics to solve business problems. While the
implementations are as individual as the companies themselves,
three areas are particularly core: customer analytics, risk
management, and operational efficiency.

 

Deeper customer engagement

Ask most financial services executives about their key
strategies for growth, and the same answers are given: increase
wallet share; improve customer satisfaction and loyalty; serve mass
market customers more cost-effectively and consistently; and
anticipate the customer’s needs so the bank can offer the right
product at the right time, through the right channel. However, the
customer base is more savvy and price sensitive than ever before,
and far less loyal.

Creating a more customer-centric strategy is predicated on
having customer segment or individual customer data available,
using the data to understand their behaviours and profitability,
and then creating strategies and plans that maximise uptake of the
bank’s products and services. For example, more effective planning
of sales and marketing initiatives; monitoring their success and
creating a closed-loop cycle for continual performance
improvement.

Business analytics software can make this work in practice
through:

   Customer segment analysis, which
enables managers to see at a glance how individual customers and
customer segments are performing across measures such as
profitability, lifetime value, risk grade and products most likely
to be purchased next. Banks can also identify the customers and
customer segments to target for product or service initiatives,
based on the performance of past initiatives.

   Predictive analytics, which enables
managers to gain deeper understanding of what individual customers
will want and when. This helps banks set strategies to acquire new
customers, reduce churn and retain the best customers, up-sell and
cross-sell more products and services, segment customers more
accurately and increase the lifetime value of each customer.

   Business modeling and financial
planning enables financial analyst, product managers and business
managers to build aligned business plans, perform ‘what if?’
analyses and plan marketing initiatives by product within customer
segments and channels.

 

Smarter risk management

Management, risk, compliance and finance executives are under
increasing pressure from governments, regulatory authorities and
business units to improve the quality and speed of risk reporting,
insight and decision-making.

They need to create an aggregated risk position that can be
analysed by risk class, product, customer, geography, function and
time horizon. This risk insight needs to inform risk decisions
throughout the bank and be incorporated into risk strategy and
appetite setting, business, capital and funding plans.

Banks rarely have a shortage of risk management expertise,
technology and data. Historically banks have devolved risk decision
making to customize systems and processes to support specific
go-to-market strategies. Combining business unit autonomy with
group governance, strategy and demonstrated compliance is the
challenge.

Implementing business analytics software can support this,
providing a platform that synthesizes disparate risk and finance
data into an integrated, enterprise-wide view of risk across
divisions, geographies and risk classes. Banks can then answer key
risk questions such as:

   What are the delinquency levels in
the portfolio?

   Which products, geographies, business
units or vintages are performing well and which are performing
poorly?

   How much of the portfolio is rolling
from one delinquency bucket to the next?

   How many new loans are being
originated, and with what characteristics, in which segments?

   Are charge-offs rising or falling,
and is one product type or geography experiencing more charge-offs
than another?

   Are receivables, delinquencies and
charge-offs in line with forecasts for these metrics?

   What’s our risk adjusted
profitability by product, geo, segment, customer, time horizon?

   Are we optimizing our use of capital
across the portfolio, customer segments, businesses and how should
our business plans change

With predictive analytics, banks can maximize customer value and
minimise risk in every interaction through every channel.
Predictive analytics makes it possible to leverage the wealth of
data available and numerous points of interaction, enabling banks
to answer questions such as which customers are likely to default
on a loan, which customers pose high or low risks, and which
customers are the most lucrative to target with resources and
marketing spend.

Ultimately, business analytics can help banks to identify the
risks that they face, take more informed decisions based on this
information, and so optimise their performance by intelligently
managing these risks.

Through building these types of risk enablement capabilities,
banks can understand and use data more effectively to making
decisions when it matters most. They can also expect significant
economic benefits from better capital management and a more
efficient allocation of resources, boosting employee
productivity.

 

Improving operational efficiency

Banks must respond to economic challenges by shoring up internal
operations to ensure that the entire organisation is running at
peak efficiency. However, making this determination requires an
in-depth understanding of how well the business is operating
relative to its historical trends, its peers and the overall
market. To gain this insight, banks need the ability to connect
operational details to business drivers.

Business analytics software can improve bank operations by:

   Providing insight into what’s
currently happening and why; eg. identifying correlations between
failed transactions/interventions; identifying process bottlenecks
by LOB, product, customers, branch, geography etc; creating
governance of key performance measures; and linking external
drivers to process.

   Delivering recommendations to improve
operations; eg. aligning operational capacity (and costs) with
business goals and revenues; optimizing pricing to more accurately
reflect interventions and volumes; and balancing service costs with
customer retention and sales objectives.

Best practice budgeting, plans, forecasts and financial
reporting are also a cornerstone of better performance and
efficiency. Becoming a leaner organisation means swapping out
inefficient processes in favor of new systems capable of driving
ongoing performance.

For instance, rather than setting targets at specific numbers,
decision-makers may want to begin tying performance targets to
events, trends and risk factors. Rather than engaging in planning
on an annual basis, they might consider adopting shorter planning
cycles focused on the achievement of specific tactics and
initiatives.

 

The analytical future

Despite the additional pressures created by the ongoing economic
downturn, banks must address the big data challenge to remain
competitive by capitalising on the increased information richness
that business analytics software can deliver.

A recent study from MIT Sloan and IBM, surveying nearly 3,000
executive managers worldwide, found that top-performing companies
are three times more likely than lower performers to be
sophisticated users of analytics, and are two times more likely to
say that their analytics use is a competitive differentiator.

It is clear that those banks that fail to unlock the value in
their data will increasingly struggle to keep up with their more
nimble and far-sighted peers.