This is a sponsored post by Kate Firuz, Product Director, PayTic

It seems that every day, a new credit, debit, or prepaid card product hits the market, each one with more bells and whistles than the last. While this is fantastic for the card holders who are collecting points and tapping their way into cash back, the work and procedures that are required to maintain the program remain largely archaic. Manual invoice reviews (or lack thereof), manual data reconciliation, and you guessed it, manual dispute filing can result in millions of dollars wasted a year and missed growth opportunities, even for small to medium size programs.

Card programs are a result of the partnering between three key players – the card network, the issuing processor, and the sponsor bank (BIN Sponsor). Only with this tri-party handshake can a fintech, credit union, or bank launch a new program, either via physical or virtual cards. So, what does it take to ensure that the program is a success? That it brings value to card holders and share holders alike.

The key to longevity, and ironically where most card programs are the weakest, is in data management. When more than one party is involved in even a single transaction, creating a transaction system-of-record to keep everyone in sync can be a challenge; and when millions of transactions run through a card program every single day, you will quickly find that you have a program that will not scale. When the data doesn’t align, and the story looks complicated, it means three things for card programs:

1. Excessive operating costs
2. Compliance and data reporting challenges
3. Inefficient dispute processing

Every month, the card networks send an invoice, billing the card program for their activity and any additional services they may have. This sounds simple enough, but mixed in with the standard line items, are often non-compliance penalty fees levied against the program. You may wonder how card programs that under-go so much vetting can act in a non-compliant way – the truth is that most of them are not even aware of the issues. The non-compliance fines are often related to data reporting and improper reconciliation. There is one simple fact that all programs must know – if your reported numbers don’t match the network’s numbers, there’s a fine for that. These “numbers” refer to a very specific set of reporting requirements including transaction count, credits, debits, chargebacks, and fraud cases just to name a few. Remember that every single action runs through at least 3 parties – the network, the issuing processor, and the core banking – each with their own file types, reporting cadence and data structures. Our clients, who represent a range from fintech to credit unions and traditional banks, have all struggled to align their data without the help of an automated system to match and parse data.

Let’s summarize the situation – in addition to customer service, dispute resolution, fraud monitoring, AML and KYC, a card program is responsible for ensuring that all their data is accurate and reported on time. When this doesn’t happen, fines result in higher than necessary invoices, and complicated invoices mean that the fines can go unnoticed, allowing the cycle to perpetuate for years.

The last, yet critical piece impacted by poor data flow is dispute management. No card program can function without proper fraud and dispute handling procedures. The data required to locate, investigate and submit a transaction for a dispute follows the same path as any transaction, plus the additional layers of going to the acquiring bank and merchant for their input. The traditional dispute lifecycle takes at least 45 days and is riddled with blind spots as the claim enters the review process. When access to transaction meta-data is available in real time and therefore the right questions are available to the processing agent, a dispute can begin and end within a matter of a few days, and usually in the favor of card program. The result of the dispute then needs to be updated in the card programs ledger, accounting system, and quarterly report. Again, delays in processing lead to delays in reporting and result in fines – the theme of the situation is quite clear!

More and more issuing institutions are turning to 3^rd party technology providers that can break through the noise and paperwork of payment program management. Automated systems that can collect, analyze, organize, and produce exceptions in seconds are showing financial institutions a freedom and confidence that was once thought impossible. With the burden of data management lifted, card programs can focus on growth and card holder value, instead of manual back-office work.

Visit the PayTic booth at FinovateSpring 2023 to learn how our automated invoice, data and dispute modules mean time and money saved instantly for your card programs.

This is a sponsored article by Jesper Petersen, CTO, 9Spokes

SMBs have long been a challenge for banks to serve well. They are often too small to offer a tailored service that they may need during times when there is opportunity for growth or when their business is suddenly challenged.

Embedded finance is rapidly becoming a new norm for SMBs in payment and banking. The segment has expanded rapidly and is expected to generate revenue of $230 billion USD in 2025. This a 10-fold increase from the $22.5 billion generated in 2020.

At the same time, the SMBs are too diverse to address in a scalable way that makes sense for the banks. Whilst there are still dependencies between the SMB and the bank, many new options are also available for the SMB, which means many find alternatives that serve them better even if the cost may be higher.

Finance is one of those areas that is rapidly evolving and embedded financial options are becoming available in applications such as point of sales and marketplaces. An example of this is e-commerce marketplaces offering real-time credit product in the form of BNPL (Buy Now Pay Later) at the point of purchase using finance providers such as Klarna, OpenPay, and Afterpay.

The funding behind these solutions in some cases come from the traditional banks but the bank has no relationship with the SMBs the service is offered to. Therefore, the bigger question here is if the relationship with SMBs is shifting away from the traditional banks to alternative providers. Alternative providers with tailored products for the SMBs to meet the demand when it emerges and to satisfy requirements where they operate.

The SMB landscape is also changing, and their skillsets are becoming stronger. People leave corporate functions and take their skills and understanding with them into the new businesses they start. A big driver for many is the desire to be self-sufficient which is the key decision point for almost 30% of new business starts in the U.S.

Most SMBs are back operating at pre-pandemic levels again. However, SMBs are not emerging unscathed from the pandemic. They know that they need to change and adapt to the demands to be able to overcome financial challenges when they emerge either through own choices or through societal challenges like Covid.

The finance market for SMBs is large and whilst more challenging to serve, it can be a lucrative market. The embedded finance options often utilize the data available in the platforms to provide SMBs with tailored solutions, to better meet their situation and need. The data they have access to means they have a better risk profile closer to real-time than a traditional bank would have.

A new range of services is also emerging embedded into the software utilised by SMBs instead of through the traditional banking route. Klarna is an example that offers lending services to its 250K customers through partners such as Liberis as an alternative to their own BNPL service.

The benefit of these services is that they are fast to access as they can make the evaluation largely with the data they access. It makes the experience of signing up and utilizing the service superior and significantly faster to access compared to traditional banking products. Furthermore, being rejected for a service has fewer consequences than a traditional bank rejecting a loan or credit card for a business.

Where does this leave us as the embedded banking services are expanding and alternative financial providers are increasing their market share significantly? Banks still have a role to play and are still serving SMBs, but they are missing out on expanding the services they provide. It is critical that they find ways to provide banking services to SMBs that utilize data to understand the real risk they are taking and enable them to respond faster.

SMBs still need their banking relationship but they seek alternative options as they struggle to get access to the financial services, they require to both survive and expand their businesses. Hence the need to find ways to facilitate better relationships using the data available and enable a real conversation about the business challenge.

This is a sponsored blog post by Tim FitzGerald, EMEA financial services manager, InterSystems

In today’s fast-paced landscape, where disruption is common and market volatility takes place with monotonous regularity, access to accurate and current data is necessary to ensure businesses can respond to changes effectively in the moment to remain competitive.

Being able to access to real-time data, and thus decrease business latency, is crucial to the competitiveness of financial services firms. Basing decisions on assumptions derived from old data imposes restraints on their ability to cope with sudden changes in market sentiment, deliver high-value services to customers, and manage risk exposure.

Research conducted by InterSystems shows that more than a third (35%) of European financial services organizations aren’t basing critical business decisions on real-time data, with just 8% of firms using data that is less than an hour old to make decisions. Given the constraints imposed by the traditional definition of intraday data, better solutions to managing, distributing, and deriving data are clearly required.

Financial services missing out on real-time data

The survey, involving almost 200 senior line of business leaders within European financial services firms, found the biggest data challenges are revealed to be delayed access to data (39%) and not being able to get the data in the correct format (33%) or from all the needed sources (31%).

Consequently, the overwhelming majority (92%) of European financial services firms are relying on data that is more than an hour old, with 85% relying on data that is 24 hours old or older. As a result, 35% of senior leaders report being unable to base decisions on real-time information and therefore forced to make assumptions, which may well be flawed.

There are multiple causes for delayed data within an enterprise, with the root often found in disparate legacy systems and applications that no longer connect to the rest of the organization. Typically, this causes pressure that then spirals to the IT department, where data-provisioning requests get stuck in a bottleneck. Forty-three percent of respondents also claimed they have anywhere between 25 and 100 data and application silos, an added complexity which further slows down their access to the required need.

But the use of intraday numbers, which can be up to eight hours old, no longer has a place in financial services. Instead, firms must now feed their frontline teams with real-time data that tracks events moment by moment to ensure they are able to respond to market changes and customer demands as they happen.

But delivering actionable data in real-time only solves part of the problem. Firms within the financial services sector must also go further and arm professionals with the data and analytics capabilities to predict what could happen next, through performing analytics on fast-moving transactional data, and provisioning access to those who need it.

Real-time data via smart fabric architecture

One solution that can be adopted uses an innovative architectural approach, the smart data fabric, which accesses and harmonizes data from existing systems and silos inside and outside the organization on demand, ensuring that the information is both current and accurate. It incorporates the ability to perform analytics on real-time event and transactional data without impacting the performance of the transactional system. This means firms can move away from querying information stored offline or elsewhere and equip themselves with real-time insights to drive their businesses forwards.

A smart data fabric architecture removes business latency and embeds agility by decoupling the reliance on old data derived via legacy methods. It achieves this by accessing, transforming, and harmonizing data from multiple sources, on demand, to make it usable and actionable for a wide variety of initiatives. It allows existing legacy applications and data to remain in place, ensuring one source of truth, and reducing architectural complexity. The ability to bridge silos from multiple sources, and from disparate locations, and allowing employees to access, query, and manipulate this data to deliver informed decision-making across the enterprise.

It also eliminates delays in accessing data and allows organizations to incorporate analytics on real time event and transactional data without impacting system performance. This is due to its distributed nature, and helps to eliminate errors and missed business opportunities. Allied to the enhanced flow of information, AI and ML can be utilized across the fabric to augment the decision-making process, delivering predictive and prescriptive suggestions while enabling programmatic decision-making when the use case warrants it.

Amid ongoing disruption, sudden market changes, and unforeseen circumstances, when the requirement for ever faster data delivery is an essential element of business success, smart data fabric architecture gives financial services business leaders a holistic view of the entire business at their fingertips so they can take a more strategic approach to their operations. Doing so gives the agility needed to not just survive, but thrive and gain a true competitive advantage in a volatile world.

This is a sponsored blogpost by JRNI.

We are in an environment of rising interest rates that will materially impact how financial institutions compete for customers. Banks and credit unions will have to embrace product innovation and relationship building as they refocus on deposit and lending services. Customer engagement will play a critical role in this change, as customers will need guidance on new products and benefits while in-person branch visits become key to establishing customer relationships.  

When we dig into the mechanisms behind how customer engagement leads to revenue, we start with how customers progress through sales stages. There are various models and stage labels, but they all have one thing in common: the customer has some sort of informational or emotional need that must be fulfilled before they advance to the next stage. The customer may be able to fulfill this need on their own through means such as independent research. However, brand engagement fills those needs faster, more accurately, and more completely. This is why engagement drives larger transactions and decreases time to transaction.

Let’s explore 5 recommendations for driving revenue through quality customer engagements:

1. Target Your Engagement and Provide Options.

The fundamentals of delivering the right message, to the right person, at the right time is an important aspect of a customer engagement strategy focused on revenue growth. The focus should be on what constitutes the ‘right’ target and the variables to reach those targets. The ‘right’ engagement is the one most likely to advance a customer along the buying journey. Early in the process, engagements focused on product demonstrations or interactive group events provide customers the information they need to feel confident in their research. Later in the funnel, engagements become more personalized as your customers’ needs become more refined. In this phase, 1:1 instructional lessons, personal appointments with product specialists or focus product tests (e.g. test driving a car), could be leveraged for customers with increased enthusiasm.

2. Treat human-to-human interaction as a high value conversion event.

“Always be closing” is a common motivational phrase in sales, but that doesn’t mean high-pressure tactics are always appropriate. Rather, the goal should be to move the customer toward a decision, even if that entails multiple interactions along the way. A one-to-many event or one-to-one appointment has higher value both to the customer and the brand because it provides more personalized and relevant insights that a customer needs in order to advance along the sales cycle.

3. Think of staff as both a revenue generating resource and a customer service resource.

A well-trained, motivated staff combine product knowledge and enthusiasm; they are your best option for advancing customers along a sales path. When you acknowledge how powerful a connection with your staff can be, you will want to set up as many engagements for them as possible while at the same time reducing their administrative burden. Real-time calendar updates, schedule visualization, intuitive data entry, and automated confirmation and reminder messages increase staff engagement capacity. Reminders for staff are just as important as reminders for customers; be sure that reminders are part of existing workflows and they contain the necessary information for appointment prep.

4. Provide staff with directional intelligence before, during, and after engagement.

Customer engagement for revenue necessitates that the staff:

1. Has information on the people they speak to
2. Understands what information needs to be provided to move them to the next step in the sales cycle
3. Has the ablity to easily collect information over the course of the engagement.

Information such as demographic data, sales history, engagement history, and customer service inquiries can all help staff paint a holistic picture of the customer. Often this information exists in disparate systems. When these systems can communicate into a centralized hub, the better prepared a staff member can be.

For example, when opening an account with a new customer, a bank representative can make observations and ask a few basic questions that determine customer needs. Young customers who are new to the area and have recently bought a home are more likely to have a family or be planning to start one than seniors. They are good candidates for auto and home equity loans and college savings plans. Older customers, on the other hand, are more likely to be interested in managing retirement funds or estate planning. Representatives should be trained to guide the conversation in the most appropriate direction based on observed and expressed needs.

5. Use engagements as intelligence for personalization.

Each engagement is an opportunity to further target the customer experience. Engagement can be used to ‘bucket’ customers according to appropriate next steps. That next step often includes a call to action for a sale but should also include additional calls to engagement. Customer engagement for revenue improves sales velocity not simply because engaged customers are more likely to purchase, but also because it recognizes that customers must be given the option to engage with the brand when it is most convenient for them, and as many times as they need, in order to convert to a sale.

Visit the JRNI booth at FinovateFall 2023 to learn how our Intelligent Customer Engagement Platform powers more engagements, less waiting, and faster revenue.

This is a sponsored blog post by Delaware Prosperity Partnership

Delaware’s status as a hub for financial services dates back to the early 1980s, when state leaders enacted the Financial Center Development Act to welcome out-of-state banks and attract new investments. Today, financial services is the state’s largest traded sector. In Wilmington alone, nearly 170,000 financial services professionals work for venerable institutions like Bank of America, Barclays and Capital One and newer firms like College Ave Student Loans, Marlette Funding and PayPal, among many others. Another 100,000 technology experts are employed in the city’s metropolitan labor market.

With that amount of fintech expertise, it made sense for Rob Habgood and his team – all veterans of the Delaware credit card industry themselves – to launch Fair Square Financial (now part of Ally Financial Inc.) in Wilmington in 2016.

“There’s a very deep talent pool here in Delaware,” said Habgood, head of Ally Credit Card and former CEO of Fair Square. “There is more credit card talent here in Wilmington, Delaware, than any other place on the planet.”

Fair Square was created as a customer-centric, digital-first credit card company and quickly became known for its competitive brand of transparent and low-fee Ollo products.

What sets the Ollo (now Ally) card apart in a state known for credit cards is its digital-first strategy. Customers do everything from applying for a card to making payments and servicing their accounts online and via the mobile app. On the back end, machine learning models and advanced analytics drive decisions from targeted underwriting to customer management and collections, with teams all working hand-in-hand to execute a strategic plan in an open-plan fintech space.

By the time it was acquired by leading full-service digital bank Ally in 2021, the entrepreneurial, stand-alone business was operating in a lean, effective and successful manner with fewer than 100 Wilmington employees serving 693,000 customers around the world. The new Ally Credit Card headquarters remain in Wilmington, and operations there are growing.

“Ally’s strong nationwide brand allows us to go after more aggressive growth and compete effectively across the full spectrum of customers. We’re going to be growing pretty rapidly here and welcoming high-quality people to continue to build our team,” Habgood said.

In 2022, Ally announced it was investing $520,000 to renovate 22,000 square feet of the Wilmington site and adding up to 150 positions – which will increase employment there by up to 200% – through 2025. Supporting the company’s investment in this expansion are a $20,000 Capital Expenditure Grant and a $2.64 million Jobs Performance Grant from the Delaware Strategic Fund.

Hiring is across the board, from marketing and product personnel to data scientists with credit card experience in analytics, risk, compliance, operations and project management. Many of those whom Ally hopes to welcome already live in Delaware or the surrounding area, but more and more talent looking for a great place to live, work and play are discovering Delaware’s advantages.

Habgood, himself, moved to Delaware in 2011. “We enjoy a high quality of life here in Delaware,” he said. “We not only have access to major metro areas, but to beaches and beautiful countryside — and to great schools.”

“Delaware is a great place to live — a great place geographically — I couldn’t speak more highly of it,” he said.

This is a sponsored blog post by Saurav Gupta, Sales Engineer, InterSystems

Financial services organizations are awash with data, and there’s a clear appetite in the sector to make use of it for a wide variety of initiatives, including analytics on real-time transactional data and reducing customer churn. But doing so requires putting the right data management architecture in place. That is rarely easy. Over the years, organizations have tried different ways to deliver consistent views of enterprise data to support their business needs but rapid changes in the demands of what their IT infrastructure and data environments need to deliver, like the implementation of data lakes and data warehouses, mean that challenges still remain.

While data within financial services organizations is often siloed and difficult to access and consume, we are now seeing the emergence of new approaches to data management that can overcome these challenges. Two of the most promising: data fabric and data mesh, are designed to help organisations leverage maximum business value from their data and existing data infrastructure.

There are many similarities between the two approaches. Both allow the data to remain stored in place at the source – a key differentiator over legacy systems that require data to be copied and moved using batch processes.

In addition, both a data fabric and a data mesh connect disparate data and applications, including on-premises, from partners, and in the public cloud, to discover, connect, integrate, transform, analyze, manage, and utilize them. By leveraging these capabilities, both approaches enable the business to meet business goals quickly and efficiently.

Points of differentiation

Despite the parallels between the two, there are also some important differences to consider here, which highlight why they are complementary rather than interchangeable. With a data fabric, the metadata, governance, and semantics are managed centrally. This structure is more frequently encountered in financial services companies that employ a Chief Data Officer that takes a top-down approach to data management.

The latest iteration, smart data fabrics, build on the data fabric foundation and incorporate a wide range of analytics capabilities, including data exploration, business intelligence, natural language processing, and machine learning directly within the fabric itself. For financial services, this means there is an ability to perform analytics on real-time event and transactional data, without impacting the performance of the transactional system. Organizations can move away from querying on offline or intraday numbers, to making decisions in the moment with real-time insights.

A data mesh, on the other hand, enables local domain teams to own the delivery of data products based on the premise that they are closer to their data and understand it better. It’s supported by an architecture that leverages a domain-oriented, self-serve design, enabling local teams to discover, understand, trust, and use data to inform decisions and initiatives and develop and deploy data products and applications.

One key difference between the two is that a data mesh allows data governance to be defined and managed at the source systems (endpoints), while a data fabric provides an overarching fabric that includes governance, lineage, security, etc., applied and managed centrally, for example, by the CDO. Looking at this in practical terms, a data mesh may be appropriate for situations where there are data sovereignty concerns, whereas a data fabric may be the right approach where the office of the CDO is defining an organizational taxonomy with access privileges.

Complementary approaches

These points of differentiation highlight the fact that the two approaches are not mutually exclusive – far from it. In fact, when it comes to determining which type of architecture to use, the selection is dependent upon the business use case. If the senior team wants to have an enterprise view of their data assets with enterprise level governance, for example, they will likely choose to implement an enterprise data fabric. If the organization wants to empower certain trusted parts of the enterprise with the flexibility to create and manage their own applications to speed innovation and digital transformation initiatives, or if data sovereignty issues are of concern, a data mesh may be an appropriate component of their overall architecture.

However, it’s equally true that, in the right circumstances, the two approaches can, and often do, work together positively to achieve positive outcomes. As one of our major financial services customers puts it: “Fabric and mesh share the same goal of easy access to data, and under the right circumstances can in fact be complementary approaches.”

Working together in perfect harmony

The reality is that data fabric architectures can co-exist with data mesh initiatives where it makes sense, such as in large organizations that must manage campaign data locally within regions.

One example where a data fabric and a data mesh work simultaneously can be seen in the demands of a large multinational wealth management firm with customer 360 initiatives.

In this use case, the company’s overall data strategy is managed centrally (data fabric), but sovereignty issues over data retention and processing are present in certain countries where local marketing campaigns are being executed. Allied to this, there is specific local knowledge of the customers in the regions, which informs variations in local campaign management. These variations are dealt with by the regional, country, or local IT teams (data mesh).

Finding a way forward

These kinds of practical examples of how data mesh and data fabric can work together to deliver tangible business benefits are ultimately far more illuminating than the debate about the respective merits of each approach.

It’s all about how the approaches can help in streamlining and simplifying business architectures so that organizations can focus on leveraging their data in meaningful ways that deliver tangible business value. Over time, we would expect to see further evolution of the two approaches with data mesh innovations in areas like domain-oriented data ownership coming together with the increasingly mature data fabric architecture. All the time though, the pragmatic focus must remain on what this combination of capabilities delivers to the bottom line. For too many organizations, data infrastructure is still seen as a cost center, but these new paradigms are paving the way for a new understanding of its value, allowing it to be appreciated in a new light as a profit center that contributes its own substantial value to the business.