Digital transformation is no longer the future of the financial services industry; it is now here. Banks, credit unions, and other financial institutions were already implementing new, digital solutions to address chronic difficulties and establish a more customer-centric approach to banking prior to the COVID-19 epidemic. That trend has only escalated in recent years, thanks in large part to the rapid growth of so-called “disruptor banks” – mobile-first, app-based Fintech firms.
Banking analytics, of all the digital tools now available, provides a mechanism to improve the customer experience, uncover chances for revenue development, and remain competitive in this increasingly turbulent market. In this blog article, we’ll go through further history and insight into the role of data analytics in banking, such as how financial institutions can profit from it, common difficulties, best practises, and more.
What exactly is Banking Analytics?
Data analytics, according to Investopedia, is “the science of evaluating raw data in order to draw conclusions about that information.” The raw data in issue can be structured or unstructured, and it can come from internal or external sources.
Data analytics can be used by businesses to achieve everything from learn more about their consumers and improve existing processes to construct predictive models and foresee growth prospects. Data analytics is a broad phrase that incorporates numerous types of analysis, including customer analytics, business analytics, predictive analytics, and so on. To that aim, banking analytics refers to any use of data analytics in the banking industry.
Data analytics has long been a part of how banks and other financial institutions conduct business; in fact, the financial services industry as a whole was one of the first to embrace analytics, using it to monitor and anticipate market movements. Banks must increasingly use banking analytics to obtain granular insights from huge amounts of data referred to as Big Data and apply those strategic findings across all levels of company.
How Advanced Analytics in Banking Can Help Banks
Banking analytics is a valuable asset to any institution and delivers numerous benefits, including:
A complete view of the customer: You may gain an accurate picture of who your customers are, what inspires them, what is important to them, and so much more by applying advanced analytics to customer data, such as which banking products they already have or who else is in their family. You can also employ sentiment analysis to determine how your customers perceive your company.
Reduced operational costs: Banks are constantly under pressure to decrease operational expenses while enhancing efficiency. In the past, financial organisations attempted to thread this tough needle by laying off employees; however, headcount reductions rarely go to the heart of the problem. Banks require a long-term plan rather than a fast cure to attain this goal.
This is where banking analytics comes into play. As previously said, analytics may be used to detect and strengthen weak points within your organisation. Using the same rationale, you may use analytics to find opportunities to cut wasteful costs.
An outstanding omnichannel customer experience: When we say, “provide the right product or service to the right person at the right time,” we are referring about personalization in action. Personalization has really taken off as a trend in the banking industry; in today’s financial landscape, 72% of customers rate personalization as “highly important” because it has the power to make customers feel seen, heard, and understood — all of which contribute to a better overall customer experience.
Another way that banking analytics improves the client experience is by streamlining existing operations. Your clients are real people with busy lives; in order to gain and keep their loyalty, you must meet them wherever they are and engage with them in a plain and seamless manner. It’s the most effective method to demonstrate that you appreciate their time.
Improved customer relations: You may establish better, longer-lasting customer connections by providing the customization that consumers seek, demonstrating that you appreciate their time and effort, and constantly searching for ways to simplify or otherwise improve the customer experience.
Customer attrition as a result of irritation and a lack of personalisation is one of the most serious problems confronting banks today. According to Forrester, only 21% of banking clients who are dissatisfied intend to stay with the bank and spend more money with it, and only 13% intend to advocate for the bank. Furthermore, 57% of Gen X and Millennial clients have stated that they would leave their financial institution to receive proactive tailored services elsewhere.
More effective risk management and mitigation: Banks can utilise data analytics to defend themselves from risk in a variety of ways. For example, for credit risk management, you may use customer analytics to divide consumers into distinct categories based on their creditworthiness. This not only allows you to select your target audience for credit products, but it also decreases your exposure to default risk because you can rely on those clients to make payments on time.
Using modern analytics for fraud detection and prevention protects not just the customer’s interests, but also the interests of your bank, saving you from potential reputational damage or retaliatory action.
Some of the important Data Analytics Use Cases in the BFSI space
Most of us are familiar with data analytics use cases in banking and finance. Do we need to reconsider them in light of the pandemic? According to our specialists, customer data is rapidly evolving, as are touchpoints. To successfully deploy data analytics in banking, models must reconsider all available data from several sources. Let us reconsider advanced analytics use cases in light of the evolving consumer ecology.
Credit risk modelling: It is not a new concept in the banking business. Traditional risk analytics models gave insights based on sources of income, loan history, default rates, credit rating, demographics, and other factors. Many other elements must be considered in addition to the basic data. Consider the scenario of consumer loans; many dynamics such as social media profiles, utility bills, monthly expenditures, and savings provide more in-depth insights into default risk. Unstructured data is also important in credit risk modelling. Deeper insights into clients’ financial well-being are provided via AI-based text analysis and consumer personas.
Enhanced Customer Satisfaction – The trickiest but appears to be the easiest to understand for everyone in the banking industry. Customer lifetime value gives information about the customer’s future revenue sources, allowing marketers to focus their efforts and reduce churn. It is difficult to predict how client habits vary over time and the major elements influencing their actions. AI-powered sophisticated models analyse patterns in data more efficiently, providing behavioural insights that humans may be unable to identify.
AI-powered Virtual Assistants – Consider insurance; a loss or damage may not occur repeatedly. It is the one point of contact for showing clients how much you care about them and making the processes easier for them. Customers now prefer self-service options to in-person interactions for processing their requests. AI-powered virtual assistants add value by addressing all information requests regarding financial services products, services, and eligibility requirements. They are also evolving to validate specific criteria based on new rules from machine learning models. It would not be surprising if an AI-powered assistant processed insurance claims in minutes.
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