Now that we've covered the topics of risk and operations, it's time to explore the world of data. In today's data-driven world, accessing and transforming the correct data into usable information is crucial for businesses across all industries, especially Fintech. Data collection, analysis, and utilization can make or break a company's success. Here's why leveraging data is essential and how Fintech companies and Partner Banks can properly harness its potential.

Using Data - For Analysis by Fintechs

Companies that effectively collect and use the correct data are often ahead of their competitors. These businesses can quickly adapt to market trends, understand customer behavior, and identify growth opportunities before others do. For example, a company might use a search engine marketing platform to identify the keywords customers associate with their brand most often. From there, they could connect this data to their social media advertising campaigns, tracking click-through rates to determine which content or products resonate most with customers.

Companies can gain deeper insights into customer behavior by mapping this data with the geographical location of their densest customer groups. Further refinement can come by integrating this data into their customer relationship management (CRM) system, identifying areas with a high volume of red-flag accounts. This enables companies to adjust their ad-targeting even further, focusing marketing efforts on geographical locations where customers are most active and have a low red-flag account percentage. With this information, fintech companies can also target ads at in-person events where customers will be present to ensure ad spending is effective and efficient.

Accurate Data Drives Smarter Decisions

Effective decision-making is only as strong as the data it's based on. Inaccurate or incomplete data leads to poor decisions, resulting in financial losses, compliance issues, and misalignment with customer needs. For Fintechs, the first step is identifying which data is most relevant to their business goals. Ensuring data quality by validating its sources, cleaning it, and using modern data governance practices helps companies maintain accuracy. This allows organizations to make confident, data-driven decisions backed by solid information. When the data becomes unreliable, the trust in the entire process could be in jeopardy. In Fintech, where financial and customer data are paramount, this level of precision is key to success. In Fintech, having reliable, real-time data is also crucial for identifying potential risks such as fraud, security breaches, or credit risks. Access to accurate and timely data is necessary for detecting irregularities more efficiently, potentially leading to severe financial or reputation damage.

Turning Data into Actionable Insights

Simply storing massive amounts of data without a clear strategy leads to inefficiency and wasted resources. Fintechs can leverage their data by integrating various sources—such as transactions, customer interactions, and external market data—into their data warehouse. By utilizing analytics tools and applying data models, companies can turn raw data into meaningful metrics (Key Performance Indicators) like customer lifetime value, customer acquisition cost, active users, conversion rate, churn risk, or fraud likelihood. Defining these KPIs ensures data is aligned with business objectives and used effectively to inform decision-making.

Break Down Silos for Better Collaboration

One common challenge businesses face is the siloing of data across departments, leading to inefficiencies, data consistency, and a lack of comprehensive understanding of the business. This fragmentation prevents teams from collaborating on a unified strategy; departments may interpret or define key metrics differently, such as using varying formulas for calculating revenue or customer churn.

A well-structured data warehouse allows for data democratization, where data from different departments—such as sales, marketing, and finance—can be combined and analyzed to provide a complete view of the organization. This improves cross-team collaboration, decision-making, and overall business outcomes.

Foster a Culture of Data Literacy

Data literacy is the ability of employees at all levels, not just data scientists or analysts, to understand, work with, and make decisions based on data. A strong data-literate culture ensures that everyone across your company can access, interpret, and leverage data to drive the company's objectives forward. 

To achieve this, companies should start by democratizing data, enabling all teams to explore and analyze data independently. Whether it's by empowering analysts or customer support teams, there are various ways to foster a culture of data literacy. However, access alone isn't enough—investing in data literacy training is critical to ensure employees can effectively interpret data and understand relevant metrics.

Using Data - For Analysis byPartner Banks

Data analytics is crucial in identifying and mitigating risks for Partner Banks. By analyzing historical data and current trends, banks can more accurately assess credit, market, and operational risks presented by their Fintech partners.

Create a Comprehensive Reconciliation Process

Transaction reconciliation is identifying and matching the record of a single financial transaction across systems. These include ensuring transactions match across internal systems of record (i.e., FBO accounts) and external sources of truth (i.e., partner's sub-ledgers or homegrown systems). Each transaction might represent a user payment, invoice, or trade reconciled between internal systems, payment processors, banks, and every step in between. Following the collapse of Synapse and the new proposed FDIC rule, it is essential to be able to track and reconcile your Fintech partner's individual customers' balances each day. It is not entirely unreasonable for the FDIC to want to know what exactly they are ensuring, but it is also an effective way to protect the consumer when the partnership is in trouble.

Always Be Aware of Your Partner's Performance Metrics

Good Partner Banks want their Fintech programs to succeed, grow, and serve their community. Analytical insights from a Fintech provide essential visibility into the Program's health, performance, and risk tolerance. Program managers may be interested in closely watching how well a program abides by contractually stipulated parameters, such as restricted use, maximum balances, and velocities. In addition, senior leadership at the FI may be looking at overall growth rates and other performance data that indicate the health and longevity of a program.

Collect Enough Data to Perform BSA/AML Monitoring

Increased regulatory scrutiny indicates that partner banks monitor and ensure program and vendor compliance with BSA/AML regulations. However, to be able to do so, it is imperative to gather demographic and transaction data from Fintech programs consistently, timely, and with integrity. Whether data is forwarded to a BSA/AML vendor or the compliance reports and alerting are run in-house, analyzing and normalizing incoming BSA/AML data helps detect data quality issues early and allows for a better response time if a problem is identified.

Using Data - To Improve Customer Experience

You can gain critical information by taking a literal or figurative seat in your Contact Center (aka "CS," Client Success, Customer Support). Let's look at some of the data you can gather and how you can apply it to improve your customer experience.

The Numbers

Review your ServiceLevel Agreements (SLAs) and/or Key Performance Indicators (KPIs) and evaluate your potential opportunities.

In case you're unfamiliar...

SLAs are the metrics you have in place with clients and/or your Partner Bank that you're expected to adhere to to avoid negative consequences.

KPIs, briefly mentioned above, are the metrics that measure how well your team is performing against your SLAs and defined CS goals.

Here are a few KPIs essential to gathering insight:

• AverageSpeed of Answer (ASA) measures how quickly your Contact Center Agents ("agents") answer live contact channels(voice/calls and chat). Shorter wait times mean customers are quick to reach an agent, and customer satisfaction is higher. Longer wait times suggest you need to be adequately staffed and re-evaluate your staffing plan.
  • ASA= (Total wait time of answered calls/ total number of answered calls)
• AverageHandle Time (AHT) calculates the time it takes to complete a customer support interaction. For Workforce Planning purposes, the AHT average across all contact types will provide a great benchmark to help with staffing plans and is an essential variable in your Erlang Calculations. (Erlang C is a mathematical equation for determining the number of agents you need to support incoming call/chat volume.) For Agent Performance, AHT can tell you a lot when coupled with performance-related metrics like Quality Monitoring and Customer Satisfaction (CSAT).
  • AHT = ((Total talk time + hold time + after-call work time) / total number of calls)
• First Contact Resolution (FCR) measures how well your team can fully meet the customer's needs during initial contact with them. If FCR is a challenge, ensuring your team is equipped with the knowledge and tools needed to resolve issues and empowered to use them can help.
• Contacts by Type isn't a KPI, but tracking contact types can pinpoint where there may be customer pain points. Contacts for activities that are available via self-serve should be below. If not, your product may product may be intuitive, and that's something to investigate. The majority of your contacts should be limited to. The most complex issues that require assistance from your support team. Evaluate and see where you have opportunities to improve your self-serve features, product complexities, and cumbersome processes.

Customer Feedback

Customer Experience Metrics lets you view your CS from the customer's lens. While direct customer feedback trends can be biased, assessing in combination with your internal metrics, like KPIs and quality monitoring, can provide a more holistic picture.

• Customer Satisfaction (CSAT) is obtained through surveys asking customers about their experience with your Contact Center team and quantifying their satisfaction with your products, services, and team. Carefully crafting the CSAT surveys is essential to ensure that feedback is actionable and impartial. Questions should be clear and measurable, and consistent rating scales should be used.
• QualityMonitoring (QM) is the internal auditing of Contact Center agents' performance in handling customer contacts. QM involves listening to call recordings or viewing transcripts of chats and email interactions between customers and agents. A scorecard assesses the agent's performance against predefined criteria for adherence to your values, knowledge of your products and services, and overall service expectations.

Look at the whole picture, and don't make assumptions based on one metric alone. Evaluating KPIs and customer experience metrics in combination will give you a better picture of what's happening, positioning you to decide where to dedicate time and resources to improving your customer experience.

If you still need KPIs and SLAs in place, you should. We recommend looking at industry benchmarks and aligning your goals accordingly. 

Remember, happy customers drive business, and the best way to keep customers happy is by listening to them (and the data)!

Check in next week for our final article, which will be published by 10/28

This is the fifth in a series of collaborative articles by iLEX Consulting Group and iDENTIFY.

About iLEX Since 2012, iLEX Group LLC has been a leader in delivering expertise in the FinTech industry, with a robust background in compliance, operations, and client management. We bring our clients'' visions to life with our ingenuity, partners, resources, and leadership.

About iDENTIFY iDENTIFY has become a leading fintech software company that provides banks with the tools necessary to unify their customer data. With several years of providing solutions for the banking industry, our vision is to streamline internal operations, create convenience for our clients, and give banks faster-to-market solutions.

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