Upcoming
Trends in Financial Analytics
Financial
analytics is the creation of ad hoc analysis to answer specific business
questions and forecast possible future financial scenarios. The goal of
financial analytics is to shape business strategy through reliable, factual
insight rather than intuition.
The
importance of financial analytics
Financial
analytics helps companies assess risks, improve processes, and focus
investments. It leverages advanced analytics and big data to reshape
problem-solving strategies and support decision-making. It evaluates product
profitability, sales channels, and customer segments, enabling growth and
anticipating future impacts. Continuous visibility into financial and
operational performance aids proactive interventions and streamlined
operations. However, automating flawed processes can result in data gaps and
poor-quality data, affecting business performance. To improve outcomes,
companies should use predictive analytics, enhance data quality, and manage it
effectively.
Types of
financial analysis
Financial
analysis involves evaluating businesses, projects, budgets, and financial
entities to assess an organization's stability, solvency, liquidity, and
profitability. It includes analysing income statements, balance sheets, and
cash flow statements to understand economic trends, establish financial
policies, plan for the long term, and identify investment opportunities.
Types of
financial analysis:
·
Horizontal
analysis compares an organization's financial performance across consecutive
reporting periods to identify significant shifts and trends.
·
Vertical
analysis examines financial statements by expressing each line item as a
percentage of another item, such as gross sales or total assets.
·
Short-term
analysis focuses on working capital, calculating turnover rates for accounts
receivable, inventory, and accounts payable to identify deviations from the
long-term average turnover rate.
·
Multi-company
comparison compares major financial ratios of two organizations in the same industry
to assess their relative strengths and weaknesses.
·
Industry
comparison compares a specific business's results with the average results of
the entire industry to identify any deviations from the industry average.
Key types
of financial analytics
Financial
analytics utilizes financial and relevant data to provide insights into a
company's past, present, and future performance. Key types of analytics for
companies include:
·
Predictive sales analytics: Using correlation analysis or
past trends to forecast corporate sales.
·
Client profitability analytics: Differentiating between
profitable and unprofitable clients for a company.
·
Product profitability analytics: Assessing the profitability of
individual products rather than the overall company.
·
Cash-flow analytics: Utilizing real-time indicators
like working capital ratio and cash conversion cycle, and employing tools like
regression analysis to predict cash flow.
·
Value-driven analytics: Evaluating a business's value
drivers or key factors necessary for achieving goals.
·
Shareholder value analytics: Assessing a company's value by
examining returns provided to shareholders, used alongside profit and revenue
analytics.
Financial analytics software
programs
As the way information is now collected and analysed
presents a significant shift -- along with new challenges software can help
reduce the complexity. Financial analysis software can speed up the creation of
reports and present the data in an executive dashboard, a
graphical presentation that is easier to read and interpret than a series of
spreadsheets with pivot tables. Popular financial analysis software programs
include the following:
·
Oracle Financial Analytics: is the modular component of Oracle's
integrated family of business intelligence software applications. It enables
insight into the general ledger and provides visibility into performance
against budget and the way staffing costs and employee or supplier performance
affects revenue and customer satisfaction.
·
SAP ERP Financial Analytics helps organizations define financial goals,
develop business plans and monitor costs and revenue during execution.
·
SAS Business Analytics provides an integrated environment for data mining,
text mining, simulation and predictive modelling -- a mathematical model
that predicts future outcomes -- as well as descriptive modelling, a
mathematical model that describes historical events and the relationships that
created them.
·
IBM Cognos Finance provides out-of-the box data analysis capabilities for sales,
supply chain procurement and workforce management
functions.
·
NetSuite provides
financial dashboards, reporting and analytic functions that allow
personal key performance
indicators to
be monitored in real time.
·
MATLAB allows
developers to interface with programs developed in different languages, which
makes it possible to harness the unique strengths of each language for various
purposes.
Trends in financial analysis
1.
Growing data footprints
Banking
and insurance enterprises have always been data-heavy. But the rise of mobile
banking and consumer demand for easy, instant banking experiences has rapidly
driven the financial services industry’s digital transformation—and with it,
massive data growth. Every transaction, click, mention, comment, and
interaction across mobile apps, kiosks, social media, and webpages is collected
and analysed.
2. Hyper-personalization
Top
of mind for banks and insurance firms is how to effectively leverage big data in
financial services to provide highly personalized offers and recommendations.
With the right tools, there’s potential for financial institutions to analyse
and manage massive datasets and predict the needs of customers on a micro
level. The shift to a customer-centric approach in financial services starts
with effective data management and data analytics at scale.
3. Inclusive banking
With
growing data footprints and hyper-personalization, banks and insurance firms
are better positioned to embrace inclusive finance a growing trend towards
suitable, accessible, fair, and equitable financial products and services. And
with those insights, banks and insurance firms can build a deeper customer
understanding and better solutions for inclusive banking and insurance
experiences.
4. Regulatory compliance
Changing
and emerging government regulations will continue to shape data management and
data analytics in financial services. Strong yet agile data management is key
for financial firms to keep up with shifting government rules and regulations
and to avoid risk and loss in the future. Relatedly, as customers become more
and more aware of, and concerned for, how financial firms manage customer
information, financial institutions may increasingly emphasize transparency to
earn customer trust, confidence, and loyalty.
5. AI solutions
With
ever-expanding data footprints and changing regulatory requirements, FinServ
will look to artificial intelligence for effective data management, data
analytics, and compliance processes. According to Gartner, banking and
investment firms will invest heavily in technology in 2022, trending towards
Generative AI, autonomic systems, and privacy-enhancing computation. AI in
finance has the potential to analyse the vast amount of growing data from many
sources, so banks can understand their customers more and provide smarter,
better experiences while also staying in step with rules and regulations in the
sector.
Conclusion:
Exploring upcoming trends
in financial analytics has been enlightening. Financial analytics shapes
business strategies with reliable insights, assessing risks, improving
processes, and guiding investments. Trends like growing data footprints,
hyper-personalization, inclusive banking, regulatory compliance, and AI
solutions offer exciting opportunities. As a student, I recognize the
importance of effective data management, compliance, and leveraging AI for
personalized customer experiences. This project has broadened my understanding
and equipped me with valuable skills for a future career in business analytics.
I am eager to contribute to the evolving field and drive positive change in the
financial sector through data-driven insights.
ISME Student Doing internship with Hunnarvi Technologies Pvt
Ltd under guidance of Nanobi data and analytics. Views are personal.
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