Data analytics has become an integral part of the finance and banking sectors. It allows firms to make better decisions and identify potential risks. Banks and other financial institutions can better understand their customers and identify potential risks by using data and Oracle Flexcube universal banking to create insights. This allows them to take action, either by offering them advice on investments or by issuing them warnings about potential scams.
This article will discuss how data analytics is used in the finance and banking sectors. We will help you understand why data analytics is essential for these industries.
Why do banks need data analytics?
Financial technology, or “fintech,” companies are the rising stars of today’s financial landscape. These startups enable banks to make vital improvements, including having their customers’ data tailored specifically for them and providing special pricing options. The prospect of these services can be tempting as they attract new clients while also increasing revenue rates by way of profits.
However, using data to enhance operations is not all that banks do. They also monitor potential risks to provide themselves with the best advice, whether it’s selling them life insurance or detecting fraudsters and scammers on a larger scale. This insight allows for dramatic changes within their businesses, which have incredible benefits.
How are banks using data analytics?
There are many ways banks and other financial firms use data analytics. However, a few of the most common ones include:
How banks and financial institutions use data analytics to manage risk
- Fraud detection
Banks should be active in preventing fraud from taking place. In fact, this is something that the public sector and various financial institutions have done for a long time. However, it’s more difficult to detect individual incidents of fraud when they’re spread across many different outlets and clients all over the world.
A data analytics solution can easily sift through countless forms of information with relative ease to identify possible anomalies among transactions regardless of location or size. This necessitates advanced data analytics to provide banks with accurate results without wasting a lot of money.
- Risk modeling for investment banks
Banks often have to consider risks they don’t fully comprehend to determine appropriate portfolio allocations. This compilation of diverse activities, such as valuations and the like, would be too complex for human staff if it weren’t performed by programs with advanced analytical capabilities. It works hand in hand with money management tools to provide investment entities with accurate assessments when making crucial investment decisions.
- Credit risk analysis
Data analytics is essential in lending. The financial services industry is based on lending, meaning that loans are the financial firms’ business. Therefore, accurate credit risk assessments have very different consequences in this case.
This makes data analytics one of the most fruitful ways to combat banking fraud. It enables banks to secure their money and all parties involved. Generally, every party is affected by bank-issued products or services that require repayment. Every transaction tends to be a direct or indirect manifestation of lending transactions, which constitutes major fraud risks in banking operations today.
- Operational and liquidity risk
As with lending, banking operations are also a business. In today’s financial services industry, banking is one of the most, if not the single most important, business sectors. Any unfortunate event or occurrence that disrupts it renders several businesses useless while forcing us to pay external firms and service providers.
Hence, many banks have begun using data analytics and Oracle Flexcube 14.x to help them decide on strategic alternatives when problems occur and how they may be tackled more appropriately. This refers to short-term liquidity risks, ranging from financial underwriting decisions (what loans will we give out tomorrow?) to credit risk management strategies for inefficient portfolios (reselling assets, products, or banking services for a profit) to market access.
How banks and financial institutions use analytics to manage supply
- Sales performance analysis
This is the most basic level. Banks or financial firms use sales analytics to help them understand the reasoning behind their customer acquisition effectiveness. Whether it is a business-to-business or consumer banking and lending service, data from billing provides valuable insight into client behavior.
This can be combined with online research of brand viewer audiences (think about how many people you know who saw your ad for a mortgage at financial sites like Yahoo!) and engagement metrics calculated by leading financial marketing agencies.
- Branch and online channel sales analysis
Banking or financial services firms use analytics to help determine branch and online channel performance. Loan processes mustn’t overlap because of a relationship of trust between the banking or financial services firm and its lending clients. The bank can tailor loans based on individual customers by using customer data from their own systems and third-party information providers such as market research companies and credit score scoring firms.
With this approach in place, you could reduce decision time at origination or have more timely access to relevant risk intelligence products. You can gain insight into relative pricing opportunities across the business.
- AI-driven chatbots and Virtual Assistants
Data analytics and AI work together. Chatbots are the new way for banking or financial services firms. Think about what is happening to customer service in retail banking, lending, credit cards, and check-cashing services. Alternative business models for these industries have created an abundance of opportunities for AI-driven chatbots as a universal customer engagement channel without huge costs involved (think along the lines of AWS).
These get smarter with every user interaction by using data analytics across pricing plans and products. They can also compare pricing or lending options. So, they are a great way to reduce customer acquisition costs. Chatbots help financial services firms target customers by using the banking information of all banking clients in the industries they wish to be more successful in.
How banks and financial institutions use analytics to manage demand
- Personalized marketing
Financial services firms can now look at their customers’ banking data with data analytics. If a client has a specific financial or credit product, it is more reason for financial services firms to use that customer data. With smarter processes and techniques like AI-powered chatbots and advanced analytics tools, digital banking allows the financial services industry to create an eye-popping personal experience.
The end result? Your bank should be able to teach them about products likely of interest, but they can also help capture customers by showing them products they might not have known existed or that could spark business opportunities.
- Lifetime value prediction
Financial services firms understand that maintaining a relationship is of the utmost importance. This is where data analytics comes in. It uses customer banking data to provide financial services firms with a deep understanding of customers. The analytics help financial services firms make accurate forecasts about pricing and products, enabling them to show only the products that potential or existing customers want or need.
With the product mix optimized for achieving higher customer lifetime value (CLV), financial institutions can now increase their revenue by giving deeper discounts on certain products while predicting future spending based on trends. While such tools have been around, they are more powerful when implemented using advanced analytics.
- Recommendation engines
As financial services firms gather customer banking data, it is of immense value. Large-scale business startups or financial institutions are not using customer banking data to drive sales. Instead, they use recommendation engines to recommend products and services that customers may want or need.
Financial services firms see great potential in blockchain technology, which comes with its ability to create decentralized ledgers where multiple users can be part of it without compromising their privacy. New developments in AI will help algorithms recommend the best deals for customers by leveraging the large volume of banking data available today.
Conclusion
Data analytics is becoming increasingly popular in the financial sector. Its popularity can be attributed to its ability to automate manual business processes. In addition, using Oracle Flexcube helps improve the overall efficiency of each business process by trapping errors that might lie within the existing model. Why not leverage data analytics and its capability to make our bank just a tad bit better?
