In the era of big data, finding ways to effectively analyze data is key. Learn how advanced data analytics enables payments companies to refine their offerings, reduce risk, and prevent fraud.
With more people transacting online, it’s easier than ever to collect customer data across channels and experiences. This data has become particularly useful for optimizing the consumer experience, detecting patterns, identifying trends, and fueling proactive strategy. For payments, the ability to capture these customer insights has enabled companies to preempt customer requests and spot fraudulent activity, before it causes damage.
Due to the vast impact of data on the payments industry, the big data Analytics market is forecast to register a compound annual growth rate of 22.97% between 2021 and 2026, according to research from Mordor Intelligence. This growing investment in big data is directly tied to the rise in fraud; Mordor Intelligence reports that finance organizations lose more than 5% of their annual revenue to fraud. As data analytics is key for successful fraud prevention, companies cannot ignore this important resource.
Data collection alone isn’t enough. Companies must understand exactly where they should be harvesting data, what questions the data answers (or prompts), how data can inform strategy, and how analytics fit into their current operations. Only then can the full value of data be harnessed.
The Added Value of Data Analytics
Data analytics can quickly organize vast amounts of data to provide critical insight for companies to optimize operations. Effective data analytics can impact all areas of the business, with specific examples outlined below.
Broadly speaking, the ability to synthesize mass amounts of data and spot patterns can be useful to both refine existing processes and inform new ones. Payments data can reveal operational hiccups, point to critical behavioral trends, and highlight new opportunities to grow business.
Data is also multi-use and multi-purpose; companies can feed the same data into multiple analytical programs to reveal different insights, building a complex and complete view of the customer. This information can then be integrated into different areas of the business or even sold to partners, for greater added value.
Specific Use Cases of Data Analytics in Payments
Within the right context, data has the potential to transform a company’s operations. With the right data analytics program, payments institutions can focus their strategy on key business areas that would benefit the most from this extra insight. This opens up the ability to be proactive, rather than reactive, and get ahead of competitors.
Lending, Payment & Transaction Analysis
Payments companies can collect and track data on customer activity to gain both a macro and micro-view of spending patterns. This can then inform which products and services to offer customers. Credit limit increases and special promotions can be more accurately extended thanks to transaction analysis, which improves the chance of conversion while also minimizing risk on behalf of the company.
When there is a greater understanding of daily customer activity, it is also easier to spot deviations from the norm on both the individual and collective level. Machine learning models can more quickly and accurately identify anomalies in customer behavior than manual review. This ultimately saves time and money while reducing costly false positives that can erode trust and cost business.
Machine learning models can also spot and alert about potential cyberattacks, enabling better reactivity times and a proactive approach to security. This level of visibility can also provide useful insights to strengthen existing prevention protocols, by noting when and how attacks are most likely to occur.
Risk Management & Investigation
There is an inherent level of risk associated with lending money and supporting digital transactions. While this can never be completely eliminated, smart use of data analytics can mitigate risk and allow companies to be more confident in their customer management. Detailed analysis of a customer’s payment behavior — from their repayment rate to the number of defaults on the account — can provide rich customer profiles geared towards minimizing risk.
Marketing & Product Management
In addition to keeping customers satisfied with their current service, strategic businesses are always looking for opportunities to upsell. Data analytics can reveal which approaches have been successful in the past and also identify new trends in customer preferences, which may inform future strategies. Data can be used to refine and tweak marketing language and product offers — the opportunities are truly limitless.
Most organizations have a wealth of data at their fingertips. The effective use of advanced data analytics enables payments companies to refine their offerings, reduce risk, and prevent fraud.