This is a sponsored post by Indium Software

2023 is bringing new regulations and transparency requirements to shape the Banking, Financial Services, and Insurance (BFSI) marketplace. This guide, Navigating the Path to Data Modernization in the BFSI Industry, explores the practical steps business leaders can take to accomplish their objectives — from identifying suitable technological solutions to effectively implementing them to maximize their influence.

By following these recommendations, you as a business leader can embark on a successful journey of modernization that not only fosters growth, but also enhances the profitability of your business.

Key Highlights

• Banking Data Modernization Challenges
• Numbers Don’t Lie!
• Data Modernization Isn’t a Brand-New Concept
• Data Modernization – The Need of the Hour
• The Journey of Data Modernization
• First Step to Data Modernization
• Data Modernization Roadmap: The 8 Pillars of a Winning Strategy
• The End Objectives of Data Modernization
• No Disruption on the Road to Digitization – Cheat Sheet: Key Tips for Next-Gen BFSI Orgs & How Can Indium Help

Read the e-book>>

This is a sponsored post by Accusoft.

Fintech software has become a critical component of the financial services industry, allowing customers to readily access financial products on their own terms while also enhancing operational efficiency. Digital technology continues to revolutionize the way financial institutions operate, and developers work hard to create new applications that can manage workloads previously spread across multiple systems and software.

Document viewing and sharing capabilities are among the most important features for fintech applications. While developers can use a variety of document lifecycle solutions to avoid the difficult task of building those features from scratch, the financial industry faces unique security and compatibility requirements when it comes to selecting integration partners. To fully understand these technical challenges, it’s important to understand the role of Java in the development of today’s fintech applications.

How Java Became So Important to the Financial Industry

Financial institutions were early adopters of computerized workflows. The first electronic communication network that made it possible to trade financial products outside the stock market floor was introduced in 1969. Computerized order flows became more widespread in the 1970s, with most institutions developing their own in-house systems. Digitization really took off in the 1980s and early 1990s following the introduction of the Bloomberg terminal and the Financial Information eXhange (FIX) protocol. In the late 1990s, the Nasdaq made it possible to execute securities trades without manual intervention by adopting Island ECN.

Java burst onto the programming language scene in 1995, and its arrival proved to be well-timed. The late 1990s and early 2000s saw extensive mergers and acquisitions in the financial industry, which left many companies struggling to integrate disparate applications and data. Java programming language, with its ability to support multiple platforms (“Write once, run anywhere” was an early slogan used by Sun Microsystems) proved to be an attractive solution to this challenge, and many financial applications were ported into Java. It also helped that Java was easy to use and orders of magnitude faster than legacy code running on outdated platforms.

Within just a few years, Java became the dominant programming language for the financial services industry. Its popularity only accelerated after the release of OpenJDK, a free and open-source implementation of the language, in 2007. By 2011, an Oracle report estimated that over 80% of electronic trading applications and almost all FIX engines were written using Java. Even now, nearly 30 years after its introduction, Java remains the dominant programming language used by financial services, far outpacing other open-source alternatives.

Why the Financial Industry Loves Java

Developers in the financial sector haven’t just stuck with Java for so long out of habit or inertia; Java’s distinctive features make it uniquely suited for the needs of financial applications, both for longstanding enterprise-grade banking systems and innovative new fintech solutions.

Security

It goes without saying that security is always a top consideration in the financial services industry. Banking and trading applications need to have security measures in place to protect financial data and personally identifiable information from unauthorized access. Java makes it easy to restrict data access and offers a variety of memory safety features that mitigate potential vulnerabilities, especially those caused by common programming errors. Oracle also continues to provide regular updates that patch known vulnerabilities and account for the latest cybersecurity threats.

Portability

As a platform-independent language, Java applications can run on almost any device. This has always been a major advantage in the financial industry, but it has proved even more valuable in the age of cloud computing and mobile applications. Developers can use the same code to deploy software in a virtual environment and make it accessible to end-users from their smartphones, computers, or other devices. Java virtual machines also support other programming languages, which further enhances the language’s flexibility.

Reliability

Since Java has been in continuous use for nearly 30 years and enjoys support from a robust development community, it has become one of the most reliable programming languages in the world. Potential instabilities have long since been addressed and there are many developer tools and documentation available to ensure that software is built upon a strong foundation. This is critically important for banking and financial applications, which require high levels of performance paired with fault tolerance.

The Need for Java-Based Document Viewing & Sharing

As fintech developers continue to build new applications that make life easier for customers and employees within the financial sector, they are increasingly finding that users expect more when it comes to viewing and sharing documents. Nobody wants to waste time and resources processing paper documents by hand, and most organizations want to avoid the security risks that come with relying on external applications for managing digital documents.

Unfortunately, today’s application users expect complex document viewing capabilities that are difficult for most developers to build from scratch. While there are several integrations available that can add document lifecycle features, most of them are not Java based and require additional development work to incorporate them into existing fintech solutions. Without the ability to support viewing, sharing, and editing natively within the Java application, users often turn to workarounds involving external programs, which creates security and version confusion risks.

Implementing Java-based Document Features with VirtualViewer

Accusoft’s VirtualViewer is a powerful HTML5 document viewing solution built from the ground up using Java to ensure maximum compatibility with fintech applications in the financial services industry while also meeting complex functionality and security requirements. With support for diverse document types, such as PDF, TIFF, JPEG, AFP, PCL, and Microsoft Office, VirtualViewer eliminates the need for multiple viewing solutions to create a better user experience within fintech software.

As a Java-based integration, VirtualViewer is compatible with almost any operating system and is both easy to implement and manage. No software needs to be installed on the user’s desktop, which allows fintech developers to roll out a scalable solution that meets their critical security and business continuity requirements within a single, high-speed application. VirtualViewer’s server component quickly renders and delivers individual document pages for local viewing as needed so users can access, view, annotate, redact, and manipulate financial documents on the fly. Since documents are displayed within the web-based viewer, there’s no need to download or transfer files, which enhances both security and efficiency.

When implemented as a replacement for a mortgage lender’s content management system, VirtualViewer made it possible to import and deliver more than half a million documents across the enterprise each day. Documents could be retrieved and viewed in under two seconds, contributing to a 40% improvement in mortgage processing times.

Enhance Your Java Fintech Application with VirtualViewer

Accusoft’s VirtualViewer provides true cross-platform document support for your Java-based applications. Whether you’re deploying your application within the cloud, on-prem, or as part of a hybrid environment, VirtualViewer’s powerful APIs can instantly provide your software with the document viewing and sharing features your customers are looking for. Installing the viewer takes less than ten minutes, and our out-of-the-box connectors make it easy to quickly connect to leading ECM applications, including Alfresco, IBM, and Pegasystems.

See for yourself: take VirtualViewer for a test drive and experience all the features available for your Java-based application.

This is a sponsored blog post by Delaware Prosperity Partnership

A new Discover Bank fund aims to increase financial health throughout Delaware while enriching the state’s innovation ecosystem and enhancing Delaware’s reputation as a hub for banking and financial services.

The Discover Financial Health Improvement Fund will support startups and early-stage technology companies that are developing solutions to improve the financial well-being of low- and moderate-income residents, communities, and small businesses statewide. Discover Bank has made an initial capital commitment of $36 million to the Fund, which was announced in June and launches this month.

“We continually explore innovative ways to support our communities in which we operate, and the initial portfolio companies in the Discover Financial Health Improvement Fund have developed technologies that improve the financial health of people with modest means and provide tools to support small businesses growth,” said Matthew Parks, Vice President of Discover Bank. “It is our expectation that these technologies can both be profitable and beneficial to the community.”

By creating a framework to drive capital investments to fintech startups, the Fund ultimately seeks to ensure that affordable and relevant financial products and services are useful and accessible to unserved and underserved individuals and small businesses. Clients for these offerings include the unbanked and the underbanked and those with low credit scores, low savings rates and/or high borrowing costs.

The mission-driven initiative is a collaboration between Discover Bank, the Financial Health Network, ResilienceVC, and Delaware-based Chartline Capital. The Financial Health Network, a leading authority in its field, will help evaluate startups for their potential impact on financial-health improvement. ResilienceVC, a seed-stage domestically focused venture firm investing in embedded fintech startups, will manage Discover’s earlier-stage investments.

Venture capital firm Chartline Capital Partners was formed under the principle that entrepreneurship and venture capital can be leveraged to improve the world. The firm invests in high-growth business-to-business technology companies serving core industries after they have started scaling their go-to-market and helps founders and management teams accelerate growth. Chartline will manage Discover’s later-stage investments.

“Throughout time, new technologies have made people’s lives better,” said Ben duPont, Chartline co-founder and Managing Director. “Chartline is honored to partner with Discover to invest in companies leveraging new financial technologies to improve the lives of low- and moderate-income people, communities and small businesses.”

The Fund has a priority focus on investing in fintech startups that are willing to operate out of the new Financial Technology Building on the STAR Campus of the University of Delaware in Newark. Fund support will then seek to spread to companies that may be located throughout the mid-Atlantic region. Companies outside the region are still eligible for funding, but the venture must be focused on materially improving financial health for consumers and small businesses throughout the State of Delaware and/or the surrounding mid-Atlantic region. Any venture focused on improving financial health – regardless of its product or service’s delivery format or specific financial topic addressed – may apply for funding.

By boosting individual startups, the Discover Financial Health Improvement Fund also will bolster Delaware’s entrepreneurial ecosystem. According to Noah Olson, Director of Innovation at statewide economic development organization Delaware Prosperity Partnership, a legacy strength in financial services, coupled with a nurturing environment for business growth, makes Delaware a great place to grow a fintech company.

“Discover, a global company with a major footprint here in Delaware, is leading by example with this new fund,” Olson said. “Adding further investment resources to a growing startup ecosystem will be beneficial for the state, as well as for the portfolio companies who are focused on financial health improvement.”

This is a sponsored article by Irene Galperin, InterSystems

Innovation is at the forefront of every financial institution’s agenda. The days when a household name was enough for a bank, investment manager or lender to win and retain business are fading in the rear-view mirror. Today’s customers are digitally savvy and are more demanding of their financial service providers.

To compete in a world where digitally-native innovators are proving successful at meeting changed customer expectations, financial service firms are expanding their analytical and automation capabilities.

The sophisticated analytics and processes required to provide customer personalization, accelerate the credit approval process, manage risk, and prevent fraud before it happens, are fueled by vast volumes of data with varying degrees of complexity. Robust, high performance data management infrastructure is crucial to advancing such innovation and remaining competitive.

To meet these many demands, a huge investment in technology is underway. Analysts at Gartner forecast global IT spending in banking and investment services will reach $652 billion in 2023 – a staggering amount. Spending on software is shifting from building in-house to buying solutions that provide quicker time-to-value.

Intelligent data management as non-negotiable

Gaining insight from data has become the new battleground in financial services, as organizations know they must make better use of their data to improve business decision-making.

Predictive and prescriptive analytics offer huge gains in responsiveness and efficiency, but before organizations have access to such insights, they must be capable of managing the vast amount of data they have – not all of it their own.

We can see how firms are tackling this in the real world, using a new approach to data architecture, which is the smart data fabric. A smart data fabric prepares data for analysis by connecting to existing sources without the necessity of moving data or creating new silos. InterSystems, for example, enables businesses to use this approach so they can gain a complete 360-degree view of each customer and their business, enabling one reality powered by unified, trusted data.

A smart data fabric pulls disparate types of data together from many sources in real-time, creating a useable, dependable, and trustworthy single source of the truth. This is no mean feat when data is growing exponentially, flowing into different, highly distinct silos, and in very different formats. A smart data fabric enables financial services firms to transform, validate, and prepare data for use by advanced applications using sophisticated analytics.

Superior customer personalization to alleviate difficulties

This kind of revolutionary approach to data is having major impact on one of the largest credit unions in the US, Financial Center First Credit Union (FCFCU). FCFCU has worked with InterSystems to build a powerful Customer 360 application that uses predictive analytics to indicate signs of financial distress. This enables FCFCU to intervene much earlier and more effectively, fulfilling its mandate to support people while building stronger relationships with members. Frontline employees are able to make more decisions themselves, and are spared the need to move between different applications. Following implementation of the new application, the organization had its best lending year, helping members defer payments and refinance loans.

Asset management transformation

Another InterSystems customer, Harris Associates, is an independent asset management firm in the U.S. with more than $100 billion in assets under management as of March, 2023. It has always looked to improve its ability to better manage risk and gain visibility into performance data on demand, using data to serve multiple consumers and use cases. Speed of access to reliable insights is critical. Harris implemented InterSystems TotalView For Asset Management to build a smart data fabric, aggregating data from third-party providers along with the full gamut of internal sources and applications.

The smart data fabric has met the all-important requirements for timeliness and consistency, serving the entire business and its clients. Business users across the firm are now able to make decisions using timely, trustworthy data, with the ability to drill down and get to the answers that matter to them. The whole project has radically improved enterprise and client reporting.

Leading fintech unlocks the value of data

Broadridge Financial Solutions, a $5 billion global fintech leader handling $7 trillion of fixed income and equities securities trades per day, undertook a significant data management transformation to build a wealth management solution and unlock the value of their data. Broadridge embraced the smart data fabric architecture using InterSystems IRIS.

The architecture seamlessly unifies data sources, creating golden source data that is distributed horizontally with a caching layer, all in one high-performance solution, helping Broadridge to gain real-time insights, agility, and operational efficiency.

The new architecture met Broadridge’s need for speed and enabled them to scale to five times current volume, handle two million transactions daily, and store seven years of data. InterSystems IRIS provided a 900% improvement in performance using only 30% of the infrastructure, compared with an alternative approach.

Broadridge’s success story highlights the importance of innovative data solutions in reshaping business strategies. In the digital era, the smart data fabric emerges as pivotal, unlocking data’s full potential for Broadridge and its clients.

InterSystems’ long record of achievement in financial services data excellence

These real-world use cases are just three examples of how better access to trusted data is revolutionizing the effectiveness of financial service firms at competing and operating in the digital age. InterSystems has a long track record of innovation and achievement in this field, and has gained Gartner Magic Quadrant recognition as a visionary for cloud database management systems. This validates the company’s next-generation data platform and innovative smart data services. Composable services remove the need to build custom applications when organizations want to become more competitive.

The ability to gain a complete, accurate view of the enterprise and of individual customers is critical in today’s highly competitive banking sector where new players often have leaner technology and greater agility. But what they do not have is valuable customer data, acquired over many years. For banks to compete, a smart data fabric provides the ability to leverage predictive and prescriptive analytics to ramp up innovation and efficiency. Organizations can gain a 360-degree view of enterprise data across many silos, enabling them to capitalize on their data assets and deliver innovative services in the face of increasing competition.

This is a sponsored blog post by Matt Roche, CEO, Extole

Your job needs to be easier.

What you want is reasonable: acquire customers at a reasonable cost that will stick around and grow to use your broader offering. Instead, you are getting lower account retention and more difficulty opening new accounts, originating loans, or signing policies. And it gets harder every year, with higher paid media customer acquisition costs (CAC) and lower loyalty.

There is a solution, Customer-led Growth (CLG), the strategy of putting your customers and account holders at the center of your marketing, and it can deliver higher quality customers at a lower CAC.

CLG works.

Customer-led Growth is executed as a coordinated set of programs and activities that activate and engage prospects and customers along the entire customer journey to drive high-quality/low-cost acquisition, higher LTV, and higher engagement. CLG is predicated on the simple fact that your existing customer base is your most valuable and underused source of brand, awareness, and growth.

Customer-led Growth delivers the highest quality customers of any channel. Extole has worked with leading credit card, credit union, bank, brokerage, insurance, mortgage, and fintech companies. In nearly every case, the newly acquired customers from CLG programs are more profitable than any other channel.

• For a brokerage, 24% more customers adopted higher-value trading products
• For a credit card company, 22% more customers made their card first out of wallet
• For a credit union, customers executed 15% to 20% more debit card transactions

In addition, existing customers that participated in programs were more likely to be among the most valuable to the firms we served. Simply engaging in programs, whether referral, nominations, gifting, cross-sell, or otherwise led to customers that were stickier and more profitable.

If a marketing approach can deliver higher-quality customers in this economic environment, why wouldn’t you do everything possible to adopt it?

The elements of Customer-led Growth

CLG is based on a simple mechanism: offer incentives to targeted audiences along the customer journey to drive high-value engagement. The key elements of a successful strategy include:

1. Evergreen referral and advocacy – Make referral an essential part of being a customer or account holder, providing codes, links, and tools for sharing that promote and reward natural advocacy.
2. Challenges – Looking to increase app downloads or get customers to set up direct deposit? Test different incentives to drive higher uptake.
3. Journey-based engagement – Introduce customers to programs at different stages, from onboarding to more mature, to keep them engaged and grow product usage.
4. Targeted offers – Target incentivized programs to audiences, like new customers, partners, agents, or specific segments to make certain that incentives are going only to those individuals that will take action.
5. Dynamic incentives – Allow rewarding using a huge range of incentives, including account credits, gift cards, charitable donations, privileges, and vouchers with rules crafted to make certain you are rewarding what creates value for you.

What to expect from Customer-led Growth

Most marketers will begin their Customer-led Growth journey with referral (or refer-a-friend) because it provides the fastest, most reliable return on investment and the highest quality new customers. Even firms with existing programs find that adopting purpose-built and modern technology results in significantly higher results because the experience is more seamless for customers, eliminating fraud and manual processing that prevent rapid satisfaction.

The next stage is optimization, tuning the incentive and experience and expanding the marketing of the program to ensure the widest possible participation. For an ordinary credit union, this could mean delivering 10% of new accounts with a basic program.

Driving new customer acquisition

In my experience, the best programs have delivered 30% to 40% of new accounts, a staggering result for a channel that delivers consistently high-quality accounts. In order to achieve this level, marketing teams must drive participation, usually through three techniques:

1. Expand marketing – The number of new accounts created is a function of customer awareness of the programs and ultimately of customers taking action. Driving higher program awareness drives end volume.
2. Segment participants – Behavioral patterns will emerge as customers engage. You will be able to distinguish simple advocates from ambassadors and superadvocates/ affiliates. Target programs to each audience to maximize yield.
3. Vary terms and incentives – Different participants will respond to different incentives, and rapidly refreshing program structures can drive higher participation and yield.

Driving customer base revenue

Once you have established acquisition programs that are effective, then you can expand to broader programs to drive customers to higher-value segments through targeted challenge programs.

For example, for almost all firms, a customer who downloads a mobile app will have a meaningfully higher lifetime value. Create a challenge program targeted to customers in their first 90 days offering an account credit for downloading and installing the app. Other important milestones include connecting accounts, executing trades, or adopting new products, all of which can be promoted at different stages using incentives that are only available to customers that are at that point in their journey.

You can also adopt “surprise and delight” style programs that offer incentives for having done something, as a thank you for a behavior that has created value. While these are more subtle, they can have a profound effect on tenure.

The long-term benefits of Customer-led Growth

A mature Customer-led Growth approach will provide a healthier, longer-term customer base that is connected with you in a more meaningful, less transactional way. As you evolve in this strategy, you will find yourselves spending less time talking about “last click” attribution, and more time talking about customer quality by channel, rates of participation, and how incentives relate to your brand. Higher quality questions reflect higher quality marketing organizations.

Extole created CLG, and is the leading platform. Connect with us September 11-13, 2023 at FinovateFall in booth 210.

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Vector illustration – Cloud computing

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.