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.

Frost & Sullivan is a growth-focused research and consulting company that offers a wealth of expertise across more than 10 industries. Frost & Sullivan’s Information & Communications Technologies Research Team conducts an annual voice-of-customer survey that contains inputs from key decision makers across industries.

What follows is an excerpt from 2022’s survey findings and research study, Customer Experience at the Core of Digital Transformation in the Banking, Financial Services, and Insurance Services Market: Transformational Growth through Digitally Enhanced Banking Customer Experience. The excerpt spotlights what respondents consider the highest customer experience (CX) priorities right now, and where their investment in CX is trending.

Definitions and Research Overview

• The banking, financial services, and insurance (BFSI) industry includes commercial banks, insurance companies, non-banking financial companies, and other entities.
• This study uses an integrated 360-degree research methodology to provide insights from end-user organizations, IT decision-makers, and influencers within the BFSI sector.
• An analyst perspective on the state of adoption and future investment plans highlights opportunities for financial services organizations to equip their workers with the advanced tools they need to achieve operational agility and interact with customers via the channels they wish to engage.
• This study also discusses opportunities for improving customer and employee experiences.

Download the excerpt >

The financial services industry has seen a breathtaking amount of innovation over the last
decade thanks to fintech applications that streamline user experiences and improve
operational efficiencies. Many of these solutions incorporate third-party viewing integrations
that allow people to view and manage documents, eliminating the need to switch back and
forth between different software.

Implementing specialized viewing technology saves time and resources during the development
process so fintechs can get their products to market faster. By selecting the right integration
partner from the beginning, they can put themselves in a position to scale capabilities in the
future without suffering unexpected costs or compromising performance.

Viewing Integrations and the Problem of Scale

Fintech developers often turn to API-based viewing integrations like Accusoft’s PrizmDoc
because they provide the tremendous power and flexibility that modern financial services
applications require. Whether it’s file conversion, robust annotation, document assembly, or
redaction, fintech software must be able to provide extensive document processing features to
meet customer expectations.

In order to implement those advanced viewing capabilities, the developer usually needs to set
up a dedicated server as part of their on-premises infrastructure or in a cloud deployment. One
of the biggest advantages of API-based integrations is that customers only have to pay for the
processing resources they use, but this can also pose some challenges when it comes to scaling
application capacity.

As fintech companies expand their services, they need to be able to deliver document viewing
capabilities to a larger number of users. If each viewing session requires the server to prepare
and render documents for viewing, costs can quickly escalate. As server workloads increase,
viewing responsiveness may be affected, resulting in delays and slower performance.

While some users may still need to use server-based viewing to access more powerful imaging
and conversion features, many customers simply need a quick and easy way to view and make
minor document alterations. Fintech developers need a versatile solution that can meet both
requirements if they want to scale their services smoothly.

Introducing PrizmDoc Hybrid Viewing

PrizmDoc’s new Hybrid Viewing feature provides fintech applications the best of both worlds by
offloading the document processing workloads required for viewing to client-side devices.
Rather than using server resources to convert files into SVG format and render them for display,
Hybrid Viewing instead converts files into PDF format and then delivers that document to the
end user’s browser for viewing.

Shifting the bulk of document processing work to client-side devices significantly reduces server
workloads, which translates into lower costs for fintech applications.

For documents not already in PDF format, the PrizmDoc Hybrid Viewing feature offers new PDF
viewing packages that pre-convert documents into PDF for fast, responsive local viewing.
By reducing the server requirements for rendering files, fintech providers can easily scale their
applications without worrying about additional users increasing their document processing
costs. PrizmDoc Hybrid Viewing also eliminates the need for separate viewing solutions
implemented to work around server-based viewing, which allows developers to streamline their
tech stack and further optimize customer experiences.

5 Ways Hybrid Viewing Enhances FinTech Applications

PrizmDoc’s Hybrid Viewing feature provides FinTech developers with several important benefits
that improve application flexibility and deliver greater value to their customers.

1. Resource Savings
   Hybrid Viewing minimizes server loads by offloading the bulk of the processing required to view
   a document to client-side devices. Reducing server requirements translates into lower costs and
   frees up valuable processing resources for other critical fintech workloads.
2. Scalable Viewing
   Shifting the processing work required for viewing to local devices allows fintech applications to
   scale their user base with minimal cost.
3. Enhanced Performance
   Offloading document preparation to the end user’s device improves viewing speed and
   responsiveness, especially for large documents.
4. Increased Productivity
   Diverting workloads to client-side devices allows application users to process, view, and manage
   multiple documents faster. Fintech developers can leverage Hybrid Viewing to provide a better
   user experience that helps their customers to be more efficient and productive.
5. Improved Storage Management
   For documents not already in PDF format, Hybrid Viewing can utilize PDF-based viewing
   packages that are significantly smaller than conventional SVG viewing files. Files can be
   pre-converted for fast, easy viewing without taking up extra storage space.

Enhance FinTech Applications with PrizmDoc Hybrid Viewing

PrizmDoc’s new Hybrid Viewing feature allows fintech developers to seamlessly scale their
application’s viewing capabilities without having to deploy new servers or rethink their cost
structure. Shifting document processing to local devices provides end-users with faster, more
responsive performance, especially when viewing lengthy documents. By keeping
viewing-related costs low, fintech developers can focus their resources on developing new
application features that help their products stand out in an increasingly competitive market.

To learn more about how PrizmDoc’s Hybrid Viewing can benefit your fintech application, talk
to one of Accusoft’s PrizmDoc specialists today.

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Photo by Francesco Ungaro