News!: Opus Technologies Launches FinGeniusAI Solutions – An Open Innovation Platform for Building Future-Ready Solutions.. Know More
News!: Opus Technologies Launches FinGeniusAI Solutions – An Open Innovation Platform for Building Future-Ready Solutions.. Know More

BLOG

Ways to Use Payment Analytics for Business Growth

December 28, 2023

Share:

Payments Data Analytics for Business Growth

The payments landscape is immensely diverse. Using payments data analytics helps strategic decisions for business growth. Read on to know how.

Globally, the volume of non-cash transactions is projected to reach 2.3 trillion by 2027 at a CAGR of 15% between 2023 and 2027. Of this, 30% of the payments will be completed using instant payment methods, such as e-money, digital wallets, account-to-account transactions, and QR code-based transactions. With the rise in transaction volumes, payments data will also see massive growth. The millions of data points generated by transactions is a rich source of undiscovered insights that can be leveraged to help businesses improve their operations, customer relations, and offerings.

Financial institutions and payment processors who sit at the center of every transaction have enormous datasets at their disposal. Don’t let this go to waste. Tap into its hidden potential to accelerate business growth and unlock fresh revenue streams. Want to know how to do so? Read on.

Types of Payment Data

The types of data points that a financial organization can capitalize on to generate relevant insights are categorized as below:

  1. Transaction Data: Details associated with individual transactions, such as amount, time, mode, location, etc.
  2. Customer Data: Data regarding the purchaser (initiator) and the merchant (receiver), such as their demography, geography, purchase history, and contact information.
  3. Product Data: Type of products and services sold and their volumes, along with the performance and demand of each of them.
  4. Payment Processing Data: Details of settlement times, failure or decline rates, fees incurred, authorization techniques and failures, etc.
  5. Fraud Data: Data related to potentially fraudulent transactions, such as chargebacks, bulk transfers, attempted payments with lost cards, unauthorized payments, etc.
  6. Financial Data: Data associated with the financial organization, including revenue, cash flow, overheads, operational costs, etc.

Payment data is available with diverse participants of the payments network. Collecting and leveraging this data within compliance parameters is key to harnessing its immense potential. Some of the most important data sources are payment processing systems/gateways, point-of-sales systems, e-commerce platforms, and external data sources, such as mobile applications etc.

The Power of Payments Data: Use Cases

Each transaction, and, in fact, every single customer interaction is a source of data. When combined with the data from third parties, it is a treasure trove of valuable insights. However, data alone is of little use. It needs to go through a complex process of organization, structuring, storage in repositories, analytics, and then report generation. These reports deliver easy-to-understand insights that facilitate informed decision-making.

There’s a lot more that can be discovered using payment data, such as:

Detect Fraud

Detecting anomalies with the help of machine learning-based pattern detection models is one of the top applications of data analytics in the payments industry. Other anomaly detection techniques, such as rule-based, graph-based, and statistical anomaly detection, can also be applied to discover suspicious activity.

Data-based anomaly detection models are dynamic and evolve with user behavior. This enables greater resilience against criminal activities. Plus, automated tools are faster and significantly more accurate than manual methods of discovering inconsistencies. Data-based fraud detection bolsters the agility of security mechanisms, leading to a robust AML infrastructure.

Benchmark Performance

Benchmarking is a great way to assess your position in the market against the competition and compare with your business’ previous performance to evaluate growth. Is payment failure on your platform is higher or lower than that of the industry? Do your engagement messages yield appropriate self-serve volumes? These are only some of the significant insights that can help you stay ahead of the curve by enabling you to discover whether your performance matches or exceeds that of the industry. Additionally, these reports help you identify areas of improvement, offer competitive pricing, and develop unique products.

The beauty of modern technologies, such as AI, is that they can recommend appropriate solutions keeping your budget, goals, and limitations at the fore. Benchmarks help you reduce chargebacks, payment failures, and churn rates. This helps you intervene before any issues escalate into bottlenecks and plug them with agility.

Discover and Predict Trends

Another popular use case for payment analytics is discovering trends and behaviors that are gaining momentum. Accurate forecasting helps you prepare for any upcoming rise in demand or scale down during “slower” periods. This optimizes resource utilization and prepares the business to cater to evolving customer needs. Also, identifying trends helps discover areas of friction, customer spending habits, and their response to the user experience (UX) and user interface (UI).

Trend discovery is essential to maintaining relevance in today’s dynamic market. Customers expect fast, easy-to-use, and reliable solutions that ensure the security and privacy of their personal information. Trend analysis helps improve customer experience and serves as a source of insight for areas that need improvement to meet customer expectations. This helps in refining business and marketing strategies to boost acquisitions and bolster loyalty.

How to Use Payments Data?

First, identify your sources of data and gain control over this data by collecting it on cloud-based repositories and organizing and structuring it to feed into analytics algorithms. Next, identify the tools and report formats that you require to effectively utilize the data and generate useful reports. After this, identify the data points and consistently refine them to gain deeper and more accurate insights. And finally, regularly assess the reports and act in the right direction to drive growth and optimize the benefits of data analysis.

Cloud-based data management is a gigantic task that requires experience and expertise to adequately define and implement data policies while ensuring compliance and security. Partnering with an experienced service provider in the payment domain can significantly accelerate data utilization, streamline efforts, and minimize costs. Contact the experts at Opus Technologies to learn more.

OPUS Organization First letter in white color

Team Opus

We’re giving you a fresh dose of insights, perspectives and the latest trends from the world of payments.

Join our mailing list to be the first to know about industry news, Opus updates & upcoming events

    Please read our Privacy Notice to know how we protect personal data