Overview on bank analytics
Digital
transformation has become a reality in the financial services sector, reshaping
the way banks and credit unions operate. As the industry adapts to evolving
customer expectations and disruptive market forces, financial institutions are
embracing innovative digital solutions to remain competitive. The rise of
mobile-first, app-based fintech companies has further emphasized the need for
traditional institutions to prioritize customer-centric approaches. In
response, an increasing number of banks and credit unions have launched digital
transformation initiatives to overhaul their technology infrastructure and
enhance the customer experience.
What is Banking
Analytics?
In
the modern era of banking, data is no longer just a by-product of operations—it
has become a valuable asset that holds the key to unlocking strategic insights
and driving informed decision-making. Banking analytics, an integral part of
data analytics, empowers financial institutions to extract meaningful conclusions
from vast volumes of structured and unstructured data. By leveraging
sophisticated analytical techniques, banks can gain a comprehensive
understanding of their customers, optimize internal processes, and capitalize
on growth opportunities.
Data
analytics encompasses a broad range of analytical methods, including customer
analytics, business analytics, predictive analytics, and more. Within the
banking sector, these techniques are harnessed collectively as banking
analytics. By applying data analytics to the specific context of banking
operations, financial institutions can derive actionable insights that enable
them to stay ahead of the curve in an ever-evolving industry. Traditionally,
banks have been pioneers in adopting analytics to monitor market fluctuations
and anticipate shifts in the financial landscape.
Benefits of Advanced Analytics in Banking
·
A 360-Degree View of the Customer: By utilizing advanced analytics, banks can gain a comprehensive
understanding of their customers. Analysing customer data, including their
banking products, household information, and sentiment analysis, provides
valuable insights into customer preferences, motivations, and needs. This
enables banks to personalize their offerings and refine sales and marketing strategies
to deliver the right product or service to the right person at the right time.
By meeting customer needs effectively, banks can increase the likelihood of
success and customer satisfaction.
·
Superior Omnichannel Customer Experience: Personalization has become crucial in the banking industry, with
customers expecting tailored experiences. Advanced analytics allows banks to
deliver personalized services that make customers feel seen, heard, and
understood. Effective personalization not only enhances the customer experience
but also reduces customer churn and drives sales. Streamlining processes
through analytics further demonstrates that banks value their customers' time,
leading to a more seamless and efficient banking experience.
·
Stronger Customer Relationships: By consistently delivering personalization, demonstrating value for
customers' time and effort, and simplifying the overall customer experience,
banks can build stronger and long-lasting customer relationships. Frustration
and lack of personalization are key factors contributing to customer attrition
in the banking industry. By leveraging banking analytics, banks can identify
potential churn risks, develop strategies for customer retention, and
proactively offer personalized services that address customer needs and
preferences.
·
Better Risk Management and Mitigation: Data analytics plays a vital role in risk management within banks. By
leveraging customer analytics, banks can segment customers based on
creditworthiness, enabling targeted credit risk management and reducing
exposure to default risk. Additionally, predictive analytics helps identify
potential fraud by analyzing customer behaviour and detecting anomalous
patterns. Early detection of suspicious activities allows banks to take
immediate action, protecting both customers and the bank from potential
reputational damage or financial loss.
·
Lower Operational Costs: Banking analytics provides insights into optimizing internal processes,
resulting in lower operational costs and increased efficiency. By identifying
weaknesses within the organization and leveraging analytics to streamline and
automate routine tasks, banks can achieve cost savings. Predictive and
prescriptive analytics can generate strategic recommendations for optimizing
processes, leading to long-term cost reductions while maintaining operational
excellence.
·
Growth Opportunities: Customer analytics enables banks to engage in targeted sales and
marketing activities. By understanding customer needs and preferences, banks
can make tailored product recommendations, leading to higher sales conversion
rates. Banking analytics also helps identify high-value customer segments,
allowing banks to focus their efforts on profitable customers. Furthermore,
data analytics opens doors to new business models and partnerships. Insights
derived from banking analytics can be valuable to other industries, enabling
banks to forge partnerships and create new business opportunities.
12 Tips for Developing a Successful Banking Analytics Strategy
When
developing a banking analytics strategy, there are a few best practices you should
follow in order to set yourself up for success:
- Start small and grow your strategy
over time. There’s no sense in trying to get everything done all at once —
attempting to do so is impractical, expensive, and unlikely to deliver the
outcome you’re looking for. It’s best to start small, figure out what
works and what doesn’t, and go from there. Best of all, you can use small
wins to finance future projects, allowing for the greatest ROI.
- Learn by trial and error. Speaking
of figuring out what works and what doesn’t, implementing a banking
analytics strategy should be an iterative process; every individual
project should be viewed as an opportunity to learn something meaningful
about how your different lines of business and, indeed, your institution as
a whole work.
- Adopt what works and eliminate
what doesn’t. We’re really just driving home the point here. That said,
it’s important to add that you shouldn’t hold on to any processes,
policies, tools, and so on that no longer serve your interests, no matter
how accustomed your employees may be to them. Sometimes it’s best to let
go of what’s familiar in favor of what’s effective.
- Build a data ecosystem using
internal and external sources. If your bank only
relies on internal data, you’re only getting half of the story. External
data can provide valuable context to internal findings, so it’s important
that you fold in external data sources whenever and however possible.
- Gather customer insights across
multiple data sets. Sometimes the most
valuable information comes from the most surprising places, so look for
insights into all possible data sets. The best way to develop a full
360-degree view of your customers by investing in a customer
data platform. This will collect
customer data across every point of contact and allow that knowledge to be
shared across the entire institution.
- Ask the right questions. In
order to do that, you first need to know what the right questions are.
This is where knowing exactly what it is you’re looking for and what you
hope to achieve really come in handy. In order to avoid wasting precious
resources, first figure out what it is you hope to achieve, and what
questions will help you glean the most information.
- Invest in OCM. Organization-wide
adoption of any digital transformation doesn’t just happen by chance — it
requires hard work, determination, and an effective OCM strategy. To
properly implement and manage banking analytics, you’ll need to follow a
structured process:
- Start by analysing the
company landscape to establish your long-term visions and more immediate
goals and metrics.
- Review and measure the
technology and training investments that you will need to make.
- Communicate the changes
to your employees, how they will be impacted and how they and the
institution will benefit.
- Invest in training to
help employees adjust to the new systems, processes, and
protocols.
- Measure outcomes to
ensure that all KPIs and ROI goals are on-track.
- Set up a feedback loop so
that employees can share what’s going well and where they struggle;
implement a support structure so that they have someone or somewhere to
turn to should they encounter issues.
- Finally, revisit your OCM
strategy at a later date to determine whether it was successful and how
to further support banking analytics adoption going forward.
- Look for systems and solutions
that are easy and intuitive to use. The fact of the
matter is that people are unlikely to adopt and use systems that are
overly complex, clunky, and confusing. When evaluating different banking
analytics solutions, look for one that uses aesthetically pleasing and
intuitive visualizations and dashboards so that data-driven insights are
easy to access, understand, and leverage. You can ensure success by
training your personnel on business intelligence (BI), reporting, and data
visualization products and services.
- Lean on executives to role model
new behaviours. Executive buy-in isn’t just necessary to get banking
analytics off the ground, it’s essential for ensuring that employees at
all levels of business get on board with new systems and strategies.
During the planning stage of your institution’s banking analytics
initiative, make it clear to executives — and those in managerial
positions — that you’re counting on them to act as role models to
lower-level employees in order to ensure adoption.
- Ensure alignment with performance
metrics, KPIs, and governance. Beyond the executive
buy-in, the entire institution will need to follow through on its
established strategies and evaluate effectiveness on a regular basis. Have
strategies in place to measure outcomes, determine which metrics are
relevant, and make any changes or adjustments needed. Keeping your systems
synchronized through good governance and master data management will keep your data updated, secure, and accessible.
- Automate as much as you can. By
automating the fulfilment of low-level service requests, such as adding
someone to an account or adding a new credit card, you can save agents
valuable time, thereby enabling them to focus on more high-level,
high-value requests.
- Assemble a winning team. When
implementing a banking analytics strategy, it’s important to have a team
of professionals that not only have data science expertise, but also
relevant industry experience to support you. Hitachi Solutions is that
team: For over a decade, we’ve been working with institutions in the
financial services sector to overcome top business challenges, deliver
personalized customer experiences, defend against cybercrime, ensure
regulatory compliance, and remain competitive, all using data analytics.
Conclusion
advanced
analytics has become an essential tool for banks in today's digital era. By
harnessing the power of data analytics, financial institutions can unlock a
plethora of benefits that contribute to their success and competitiveness in
the market. From gaining a comprehensive understanding of customers to
delivering personalized experiences, banking analytics empowers banks to meet
customer needs effectively, reduce churn, and build stronger relationships.
Moreover, it enables efficient risk management and fraud prevention, protecting
both customers and the bank's reputation. The optimization of internal
processes through analytics drives operational cost reductions and increased
efficiency. Additionally, analytics identifies growth opportunities by enabling
targeted sales and marketing strategies and fostering new business models and
partnerships. Embracing advanced analytics in banking is no longer an option
but a necessity in today's rapidly evolving landscape. By leveraging
data-driven insights, banks can navigate challenges, deliver superior customer
experiences, and position themselves at the forefront of innovation and success
in the financial services industry.
ISME Student Doing internship with Hunnarvi
Technologies Pvt Ltd under guidance of Nanobi data and analytics. Views are
personal.
#BankingAnalytics #DataDrivenInsights
#CustomerExperience #Personalization #OperationalEfficiency #RiskManagement
#FraudPrevention #CostReduction #GrowthOpportunities #DigitalTransformation
#CustomerRetention #DataAnalytics #FinancialServices #InternationalSchoolofManagementExcellence
#NanobiDataandAnalytics #hunnarvi
Comments
Post a Comment