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Using Data Analytics in Finance: 4 Key Benefits

“Information is the oil of the 21st century, and analytics is the combustion engine.” - Peter Sondergaard

Consumer needs and spending habits vary tremendously over time. So whether it’s convincing clients to adopt your services for the first time, retaining them over the long haul, or hoping they’ll become advocates of your services — understanding their needs and behaviors is extremely important.

Enter the world of data analytics.

Everyday financial behaviors not only inform current business decisions — they offer key customer insights. Simply put, there is a reason financial institutions collect information about credit card transactions, customer service calls, and mortgage applications.

So, what are data analytics?

At the broadest level, data analytics are “a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making” (Wikipedia). In the context of financial services, data analytics are used to help businesses better understand their customers, so that they can tailor their offerings and make better informed decisions.

Let’s look at some of the key ways companies use data to optimize business performance and gain customer insights.

Improving Customer Experience

Data analytics provide financial institutions with detailed insights into their customers’ needs, wants, behaviors, and pain-points. Using big data from call centers, credit card statements, and online behavior, banks and other financial institutions better prepare their customers for life events – getting married, kids going to college, buying a home, etc. This allows companies to understand their audience’s present and future needs with the assistance of complex equations and analytics.

Personalized Communication

From relocating for a new job to getting engaged, customers and their financial needs are always in flux. Advertising and providing the right kind of loan, savings account, or credit card at the right time makes financial institutions appear clairvoyant. Of course, a well-timed ad is anything but magic — it’s based on carefully collected and processed data via AI and machine learning. The more personalized the services and benefits, the happier the customer, and the more likely they are to remain loyal to their existing bank.

Fraud Detection and Protection

If you’ve ever ordered something from a suspicious website, or even spent money in another country, you may have experienced a frozen account until your bank could confirm your legitimacy. With the growth of data analytics and improvements in algorithms, companies are better than ever at detecting out of pattern behavior, protecting customers, and ensuring their services are secure.

Data analytics also helps financial institutions with maintaining compliance with the know your customer (KYC) guidelines. KYC requires financial services professionals to make a concerted effort to verify the identity, suitability, and risks involved with establishing & maintaining a business relationship with a client or customer. By using the right analytics tools, financial institutions can reduce their KYC costs while remaining fully compliant.

Competition Differentiation

Using the tools of big data in financial services, institutions can keep themselves up-to-date with changes in shifting consumer trends by continuously collecting and analyzing data. Keen use of data-analytics can also help differentiate a company from its competitors, allowing it to retain its customers through their college loans, mortgages, investments, and eventual retirement.

Using Data Analytics in Finance

Data’s value is in what it tells you and what you do with it. Data analytics helps companies piece together digital narratives, which allows them to better understand, assist, and protect their customers. With this information, financial service providers will continue creating personalized services for their customers today, tomorrow, and years in the future.

Topics : Articcles, Data Analytics, Financial Services, Technology

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