With the rapid development of digital technology, banks have started to implement business analytics to automate their business processes, maintain customer satisfaction, and drive revenue.
If ever there’s been a sector where analytics could play a stellar role, it’s banking. Data is something that banks have been dealing with for ages, and its scientific analysis can help them bring about significant performance improvements, leading to a boost in their bottom lines.
What is Finance Analytics?
Financial analytics, also known as finance analytics, offers various perspectives on the financial data of a hypothetical business, providing insights that can simplify strategic decisions and actions to enhance the business's overall performance.
Where can Financial Analytics be used?
The primary way analytics can be used is in serving the customer. Today, at the top of the customer pyramid is the millennial generation, whose idea of banking is far different from that of its predecessors. Statistics show that millennials will surpass ‘Baby Boomers’ in 2019.
There are about 90 million millennials in just the USA alone, representing a collective buying power that runs into trillions annually.
So it’s obvious that for banks to survive, they need to cater to this segment, which means adapting.
They need to dump the old money management mindset and techniques to better connect with this customer base.
Which means smartphone apps, smart credit cards, and so on. And this also means introducing finance tech, including behavioral analytics, to “know your millennial customer”.
Financial apps are slowly but irrevocably replacing the bank teller or bank officer at one level, simultaneously helping banks to reduce operational costs.
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Invariably, when banking and analytics are spoken of in the same breath, the reference is to use data analytics for:
- Customer experience
- Compliance
- Risk Management
- P&L
World over, banks have been under tremendous pressure. More so, in the US, after the financial crisis in 2008, banks have started looking at retail for growth, so adding value to a customer’s experience is a top priority.
A standard, ‘catch-all’ marketing policy, for example, is no longer enough. Banks have realized they need to “know” their many customers at a granular level.
How? By relying on the vast amounts of data they collect on them, almost daily.
Analysis by humans a few years ago was subsequently impossible. Still, today, with Fintech, this data can be analyzed to reveal customer habits, preferences, and needs, leading to better engagement between the bank and the customer.
Most banks have realized that they have no option but to change. Evidence of this was in a recent McKinsey survey that found that almost every bank had advanced analytics among its top five priorities.
The report also pointed out a pertinent issue – a few banks that had gone down that route had already started reaping rich dividends.
This motley group had invested in data analytics tech by establishing data lakes and centres of excellence and using machine learning (ML) techniques.
Why is Financial Analytics Important?
Financial analytics benefits in shaping future business goals. You can also develop the decision-making strategies of your business with the help of Finance Analytics.
Financial analytics provides in-depth insights about the financial position of your business and increases its productivity, cash flow, and value.
Customer Experience
It’s all about the customer, customer, and the customer. Analytics can be used for customer segmentation.
After all, banks do have all kinds of customers with different financial behaviors and requirements.
Using Big Data, banks can segregate clients based on their demographic profiles, behavior, including buying or investment patterns.
Such segmentation will benefit banks by helping them target audiences with marketing promotions and build better customer relationships.
It does not end there. Banks can study the spending patterns of their clients and, using predictive analysis, identify when potential customers may require certain financial services.
As a first step, banks do not need to invest in costly finance analytics platforms that require experts from the “outside”.
A Harvard Business Review white paper found that 62% of organizations required others within their firm to perform some steps in the analytics process, resulting in 69% dissatisfaction with the quality of the output and 81% dissatisfaction with the speed of production.
Clearly, dependence on data scientists and specialized staff for data preparation and analytics is not everybody’s cup of tea. The answer lies in self-service data analytics tools.
Such tools can be deployed at the departmental level, where bank employees themselves can be trained to operate them.
Sharing departmental banking data analytics on a day-to-day basis will help the bank gain a comprehensive view of all customer interactions, enabling effective customer targeting.
Risk Management
Another very important area where finance analytics is already playing a role is in risk management within the banking sector. Risks come in many forms – bad loans, fraudulent activities, or failed investments.
Of late, global banks have been under great strain due to increased competition from non-banking players, low asset yields, and an increase in commercial borrowings.
All these factors represent a percentage of risk for the bank, and early detection of these risks can help a bank prevent a significant loss.
After all, as competition for borrowers intensifies, banks tend to underwrite more loans to companies with looser lending restrictions.
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Analytics can help mitigate risks
To sum it up, while clear evidence exists that banks have started veering towards finance analytics/financial analytics to keep their flag flying and their ship from getting mired in debt, there are still significant improvements needed.
Too many banks continue to save their data in silos, spread across various customer touchpoints like an app or an ATM.
Information and its subsequent analytics need to be amalgamated and then shared to allow line account managers and product leaders to obtain a complete view of customers – retail and commercial – to assess better the risks associated with individual customer activities and portfolios.