Why the Financial Sector Loves The Practice Of Analytics?

The rapid proliferation of digital services in the banking sector fostered by both traditional incumbents as well as new generation fintech companies has resulted in a massive surge of data being generated across the financial ecosystem. And now, from customer experience to improved revenue monetization, financial enterprises are in a race to extract the maximum ROI from their data landscape. In a sense, they are all on the same journey of data analytics.

The financial analytics market is estimated to grow to nearly USD 11.4 billion in 2023 globally according to some studies. As more BFSI players realize the value the hidden data within their operations can unlock, greater investments are bound to happen. In fact, numerous financial and technology companies are individually as well as collectively building dedicated Centers of Excellence (CoE) for analytics.

In the face of so much activity, it is natural to have questions about why there is a surge in demand for analytics in the financial sector. In essence, the buzz is driven by the promise of the practice of analytics. There are many potential benefits, some already visible and others seemingly in the offing.

Let us examine the possibilities that analytics can uncover for businesses in the financial sector:

Improved Targeting

New customer acquisition is often a challenge in the face of a super-competitive market. Winning this battle needs time and patience. However, revenue growth cannot wait. This is where banks and financial companies can leverage analytics to learn more about their existing customers and scale up revenue realization by offering them services that have higher chances of acceptance based on demographic or customer segmentation trends. Analytics helps in customer acquisition as it tells them the kind of customer audience to target and the message most likely to resonate with them. Intelligent data analytics helps maximize revenue opportunities as it helps banks predict and create a pool of customers
more likely to avail upsell and cross-sell options like top-up loans. Analytics allows financial services companies to drive targeted campaigns more likely to capture interest and convert into deals faster.

Higher Degree of Personalized Engagement

In a time when customer experience has become a key service differentiator, the task for banks and financial services companies is to always be relevant in the eyes of each individual customer. Customers want to be exposed to only those offers, options, and opportunities that are “right” for them. They want the assurance that their financial services provider knows them and understands their specific objectives. To be able to add such specificity and granularity in customer communications financial enterprises need clarity on the individual preferences and needs of customers. This is where data analytics plays a key role. With analytics powered by machine learning capabilities, it is easier to prepare personalized offerings and deliver them to customers when they need them on the channel of their choice. Analytics enables them to drive continuous customer engagement initiatives that promise better response and faster ROI. Data-driven insights help in picking the right content, channel, time, and medium to engage customers.

Streamline Customer Support

Customer support in the banking and financial sector has long been saddled with a bad reputation and is sometimes shown as the poster child for poor user experience. This can be changed considerably by employing data analytics. Analytics can analyze the history of issues and incidents that have been reported in the past and help the financial institution prepare better mitigations and define specific support strategies for different customer categories. Intelligent automation can quickly deliver answers and solutions to new customers for similar incidents as and when they occur. Furthermore, the insights
generated through analytics can help power intelligent chatbots which eliminate wait time for incident resolution and improve customer engagement. This will ultimately help in improving customer loyalty and retention.

Dynamic Revenue Accounting and Pricing

One of the key criteria for customers to avail of loans and other financial services is the ability of providers to offer them suitable rates. However, banks too have their own operational and market-linked calculations for revenue forecasting and realization. Pricing schemes tend to be driven by business exigencies rather than customer concerns. With intelligent analytics, banks and financial enterprises can use data to create better revenue models that balance internal business profitability and customer expectations on fees, interest rates, and other service costs. With the right pricing models, banks can offer niche products that may have lesser margins, but higher sales thereby improving outcomes through volumes.

Risk Management

The financial sector has the most critical focus on security risk mitigations. With most banking and financial enterprise systems handling data like sensitive customer credentials, social security, and personal financial elements, it is imperative that risky behaviour be identified and eliminated. With data analytics, financial enterprises can get greater control over operational risks to their transactional systems. AI-powered analytical solutions can compare historic data with real-time transactional data to identify suspicious activities such as fraudulent transactions, irregular spending or deposit trends, attempts to override security policies, and much more. They can be used to trigger automatic notifications to stakeholders so that they can take necessary actions to mitigate any possible risks. Financial platforms can be isolated securely preventing any fraudulent activity from proceeding and causing further damage.

It’s apparent that banks and financial institutions can unlock hidden value from their digital channels by leveraging the several facets of data analytics. Relying on data-driven insights helps decision-makers take the right choices which ultimately translate into better profitability, lower risks, and increased customer satisfaction. It is important to pick the right strategy, identify quality data generation roadmaps, and invest in the right technology to succeed in data analytic initiatives. Investing and setting up dedicated CoEs for financial analytics can help drive both short- and long-term benefits that justify the investment. Get in touch with us to know more.