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Finovate Blog
Tracking fintech, banking & financial services innovations since 1994
Finovate webinar, in collaboration with InterSystems, Thu 12 Oct, 3pm BST / 10am ET
How can financial services firms improve the client journey and experience? Next-generation technologies that advance digital transformation are at the heart of that answer, but these rely on the firm’s ability to gather and analyze all available data across the business.
By now, many in financial services will have adopted technologies such as AI, ML, and analytics to enhance the experience their customers receive. However, to reach their full potential, they depend heavily on a solid data technology platform to build, train, and continuously improve model quality and predictions.
Hyper-personalization can foster loyalty in an era in which loyalty has declined, and it pushes customers and investors towards those firms which can be agile in what they offer. This level of hyper-personalization offers immense growth opportunities for all providers if they can cater to small and specific groups – but on a large scale.
Watch this webinar and learn from the experts:
How to achieve competitive differentiation by delivering better customer experiences.
How to optimize your existing infrastructure with a smart data fabric.
How other firms are building these data foundations and the results of their deployment.
On the panel:
Joe Lichtenberg, Global Head of Product and Industry Marketing, InterSystems
Virginie O’Shea, CEO and Founder, Firebrand Research
This is a sponsored post from Tim FitzGerald, EMEA Financial Services Sales Manager, InterSystems.
Innovation undoubtably will help firms keep up with market volatility, changing customer demands, and the competition – not just today, but in the future. This is reflected in the thoughts of financial services leaders themselves as almost three-quarters (73%) believe innovation is vital to their survival as a business. Yet, despite widespread recognition of the critical nature of innovation, financial services firms are facing difficulties in successfully executing their innovation initiatives.
In particular, firms cite skills gaps and integrating disparate data sets as significant barriers to innovation. With the uncertainty and upheaval of the last few years showing no signs of slowing down as we head into 2023, finding ways to better leverage their people and data to further innovation, therefore, must be front of mind.
Obtaining a 360-degree view
Data has a vital role to play in innovation initiatives. Being able to access and use accurate, real-time data from all business units to obtain a holistic 360-degree view of the enterprise and its customers will enable firms to better identify and respond to growth opportunities, address challenges in an agile manner, and make more informed, in the moment decisions. This requires firms to address the data integration challenges they are currently facing and connect their myriad data and application silos.
One way of doing this is by adopting a smart data fabric which accesses, transforms, and harmonizes data from multiple sources, on demand, to make it usable and actionable for a wide variety of business applications. Ideal for complex data environments, the smart data fabric eliminates delays which lead to errors, missed opportunities, and decisions based on stale or incomplete data.
This approach allows existing legacy applications and data to remain in place, thereby enabling firms to maximize the value from their previous technology investments, including existing data lakes and data warehouses, without having to “rip-and-replace” any of their existing technology.
By obtaining this instant insight into their organization and customers, financial services firms will be able to make better, more accurate decisions to drive innovation, improve customer experiences, and get ahead of the curve.
Power to the people
Implementing new technology alone is not enough to help firms overcome the barriers that are currently standing in the way of successful innovation. People also have a significant part to play in innovation initiatives, so giving them the capabilities to conquer current skills gaps and to use data effectively to drive innovation are also key. Firms can achieve this by implementing a holistic innovation strategy which brings together all the critical elements required for successful innovation – people, processes, and technology – and identifies how to empower business users with data.
By putting data directly into the hands of business users, firms will be able to mitigate some of the impacts of skills gaps and help people to actively contribute to innovation initiatives. Self-service analytics capabilities embedded within smart data fabrics will provide immense value here. These capabilities will enable business users to freely explore the data, ask ad hoc questions, and drill down via additional queries based on initial findings.
In doing so, not only will firms be able to leverage their data more fully, but also they will be able to mitigate the impact of skills gaps by empowering employees to read and interpret data and make the data-driven decisions needed for successful innovation. This also will reduce reliance on IT teams to surface and interpret data, while avoiding the need for business users to learn a whole host of new skills and tools.
New year, new approach
As firms look to 2023, likely with a mix of excitement and trepidation about what the year may bring, ensuring they address the barriers currently standing in the way of innovation success is essential to help them respond to whatever comes next. By addressing issues with data integration and skills gaps head on, financial services organizations will be able to make more effective use of both their data and people to drive forward innovation initiatives.
Arming themselves with a clear innovation strategy and a team of empowered and data-enabled employees will give firms the capabilities overcome any challenges that may arise, but also critically, to grow their offering, future-proof their organization, and meet changing customer demand. Ultimately, adopting this approach will help firms to set themselves up for long-term innovation success, not just for 2023, but beyond.
This is a sponsored post by Tim FitzGerald, EMEA Financial Services Sales Manager, InterSystems
The use of analytics within the financial services sector has evolved over the years, with some suggesting that it could be about to evolve even further, moving from a landscape where decisions are “data-dictated”, rather than “data-informed.”
There is a distinct difference between the two concepts and the role, or lack of, that humans play in each scenario. In the case of data-informed, humans remain in the loop to make decisions and take the appropriate actions based on data and analytics, whereas data-dictated refers to applications executing programmatic actions automatically in response to some stimulus or event.
So, are financial services organisations really at a point today where human insight is no longer a vital requirement of the decision-making process and are there really just two types of data-related decision-making at play? In short, no. But it’s not completely black and white, as discussed in a recent Economist Intelligence webinar. Instead of just two options, today’s financial services firms typically implement four different categories of analytics: panoramic, predictive, prescriptive, and programmatic. Depending on the use case and the organisation, each of these types of analytics provide businesses with immense value.
Panoramic, predictive, prescriptive, and programmatic
Firstly, panoramic is about providing the business with a real time, accurate, expansive view of what’s happening inside and even outside the organization. For financial services, that might be the real-time liquidity across an entire firm.
Predictive, on the other hand, calculates the probability that events are likely to occur. For example, what’s the probability the Bank of England will cut interest rates if inflation pressures ease, as has been mooted, and how will this impact the firm’s positions?
Prescriptive analytics analyzes data to suggest the most appropriate actions to take, based on what is likely to occur, or what is already happening. This type of analytics would allow an investment bank for example to continuously predict the probability that their total market exposure will breach their risk utilization limits. With the right data and analytics platform in place, firms can also obtain prescriptive guidance that presents various options they can take to prevent or eliminate a breach, with the expected outcomes and trade-offs associated with each option.
These insights allow risk managers, who tend to have extensive experience in handling these kinds of situations, to make decisions based on their experiences, and guided by data-driven prescriptive analytics. For instance, it can help them to determine whether to initiate a hedge or unwind some positions. Prescriptive analytics therefore ensures experienced experts remain in the loop and at the heart of decision-making, rather than actions happening programmatically.
The final of the four Ps is about executing real time programmatic actions based on predictive and prescriptive analytics. Often, programmatic analytics are employed when there’s no time for human intervention, for cases like fraud prevention, pre-trade analytics, trading, and customer next-best action. Programmatic actions are also deployed in use cases when there’s simply no need for a human to be in the loop, which allows the organization to streamline operations and improve productivity.
Pragmatic application of the four Ps
Consequently, rather than moving away from a data-informed (human in the loop) to data-dictated (no human in the loop) state, the financial services sector is instead opting for the pragmatic application of any or all of these four Ps of analytics.
This use of analytics is providing firms with the capabilities needed to gain a 360-degree view of enterprise data, delivering a wide range of benefits to the business including better compliance, increased revenue generation, and improved decision support. When financial business leaders are empowered by real-time data and analytics, they are able to make decisions based on accurate and current data, not data that is weeks old, thereby eliminating errors and missed business opportunities.
Additionally, by incorporating advanced analytics into real-time processes flows, dashboards, and reporting, businesses can obtain better insights to guide decision-making, helping to understand what happened, why it happened, and what is likely to happen.
Armed with a current, trusted, and comprehensive view of what’s happening in the moment ensures financial services firms are prepared for events and disruptions that are likely to occur, can manage events and disruptions faster as they arise, and are in the best position to take advantage of new opportunities as they present themselves.
This is a sponsored post by Tim FitzGerald, EMEA Financial Services Sales Manager, InterSystems, Gold Sponsors of FinovateFall 2022.
In today’s fast-moving landscape, financial services firms are under increasing pressure to remain competitive and generate more revenue by developing new products and services faster, while still leveraging their existing resources.
In recent years, this has seen many financial services organisations turn to external fintech solutions to help accelerate innovation and quickly obtain new digital capabilities. And so, fintech partnerships have become critical components of financial institutions’ growth strategies, rather than the technology experiments they started out as.
To ensure innovation success, it’s vital that financial services organizations can easily leverage and provision new fintech services and applications by seamlessly integrating with their existing production applications and data sources. But the true value and potential of fintech solutions can’t be unleashed until integration is quick and easy.
As many firms will attest, arduous and costly integration can see the value of such initiatives dwindle before their very eyes – sometimes to be lost altogether. Common challenges can range from unforeseen issues tying up precious IT resources, to costs spiraling out of control and timescales sliding drastically from what was planned or what is desirable. Ultimately, these delays can result in the loss of any competitive edge as rivals launch similar solutions much faster.
Ensuring successful integration
Fintechs have become increasingly attractive as they incorporate the latest technologies, modern application methodologies, and deployment platforms. However, for banks to make effective use of these opportunities, those technologies need to be woven into its existing infrastructure, much of which is likely to be based on legacy technology.
Consequently, successful integration requires an understanding of the intricacies and idiosyncrasies of those legacy systems. It also demands knowledge of the underlying data architecture and how to connect the new technology to systems that weren’t built to be connected to in such a way. While this isn’t an unsurmountable problem, getting it right will take resources, budget, and time.
Careful consideration is also needed when undertaking the integration to ensure that the resulting architecture doesn’t become overly complex. After all, if it comprises multiple technology layers from different vendors, all with differing versions and releases, any future change could impede the bank’s ability to take advantage of the benefits they set out to achieve.
Next will be to determine how data from existing systems will be fed into the new system and in what format. To get around this, it’s all too easy to layer extraction tools upon a myriad of other tools, including transformation tools, data lineage, master data management, databases, and data lake technologies. However, what firms are then left with is a multi-headed monster that no one person truly understands. This approach to data integration is also complex and costly to design, deploy, manage, and maintain. Fortunately, adopting a smart data fabric approach, a next generation architecture, can provide a way for financial services organizations to overcome these challenges.
Achieving bidirectional connectivity
By leveraging a smart data fabric, it is possible for institutions to connect and collect real-time event data and obtain unmatched integration capabilities using just one holistic platform. This approach eliminates the complexity and inefficiencies of manual integrations and other legacy approaches to integration and enables firms to integrate applications faster and more efficiently. It does this by essentially creating a dynamic real-time, bidirectional gateway between cloud-based fintech applications and their own production applications and data assets.
The smart data fabric integrates real-time event and transactional data, along with historical and other data from the large number of different back-end systems in use by financial services organizations. It transforms the data into a common, harmonized format to feed cloud fintech applications on demand, thus providing seamless, real-time, bidirectional connectivity and integration with the bank’s existing legacy enterprise data, production applications, and data sources.
Not only does this help firms to realize faster time to value and achieve simpler implementation that is easier to maintain, but it also gives financial services institutions the agility needed to innovate faster and keep critical initiatives on track. Additionally, it helps to futureproof their architecture by making it easier to incorporate any fintech applications and technologies available in the marketplace, thereby empowering them to react to new opportunities and changes in their environments.
Ultimately, there is immense value to be unlocked from fintech solutions and applications. However, that is only possible through swift and simple integration. By implementing a smart data fabric-enabled data gateway, financial services organizations can quickly and easily integrate new solutions within their existing infrastructure to ensure they are able to keep pace in a rapidly evolving landscape.
This is a sponsored post by Ann Kuelzow, Global Head of Financial Services at InterSystems.
A staggering 86% of financial services firms globally are concerned about using data to drive decision-making within their organizations, according to the latest research from InterSystems of 554 business leaders within financial services companies, including commercial, investment, and retail banks, across 12 countries globally. This lack of confidence largely stems from an inability to access data from all the needed sources and the time taken to access data. Given the wealth of data financial services firms have, this is a major concern, with the potential to open organizations up to risk and severely impede key business initiatives. In fact, more than a third of firms in the survey cite the primary impact of these challenges as being difficulty in gaining a 360-degree picture of customers.
As competition intensifies within the financial services sector, customer 360 is something that all firms must confidently be able to obtain. Doing so will empower firms to provide clients with the products, services, and hyper-personalized, real-time experiences they have come to expect across all aspects of their lives. But this relies on gaining access to accurate, consistent, and real-time data encompassing all touchpoints. Consequently, firms must first address underlying issues with their data architecture.
Solving data challenges
Gaining a holistic view of the customer requires firms to pull together all available information on each customer. As customers are likely to interact with a variety of different departments and personnel within the firm, this information can be spread across multiple systems and silos, including trading, savings, credit cards, loans, insurance, CRM, support, data warehouses, data lakes, and other applications and silos, as well as data from external sources and suppliers. The data is often in dissimilar structures and formats and follows different naming conventions and metadata. Therefore, making sense of this dispersed data typically requires significant effort and expense, and using it to make informed, accurate, and fast decisions is a major challenge.
As organizations look to solve these problems, data fabrics, a next-generation architectural approach, have emerged to provide financial services firms with a way to speed and simplify access to data assets across the entire organization. It does this by connecting to existing systems and data silos containing relevant data, both inside and outside the organization, and ingesting the relevant data on demand as it’s needed. It accesses, integrates, and transforms the data as it’s being requested, providing a real-time, consistent, harmonized view of the data from different sources, all from a single view. This allows firms to gain a complete 360-degree view of the customer.
Going a step further
A smart data fabric takes this approach a step further by providing built-in analytics capabilities which enable business users to understand customer behaviors and actions better and even to predict the likelihood of future behaviors, such as purchase of new services, churn, or response to targeted offers. It also provides the business with self-service analytics capabilities, so line-of-business personnel can drill into the data for answers without relying on IT, eliminating the usual delays associated with adding custom requests to the IT department’s queue.
This next generation approach also helps solve latency issues, as smart data fabrics lets the data reside in the source systems, where it’s accessed on demand, as it’s required.
Adopting this approach will help to restore firms’ trust in their data, ensuring that they can quickly access consistent, reliable, and accurate information on which to base decisions, fuel data initiatives, and build up a comprehensive view of the customer.
Elevating the customer experience
Being able to leverage the wealth of customer data inside and outside of the organization for customer 360 will empower firms to offer a vastly improved customer experience. For instance, with a single view of the customer, advisors, help desk, and support teams will be able to provide customers with the immediate answers and recommendations and thereby enhance their interactions with the organization.
Armed with customer 360, firms will also be able to increase revenue streams by predicting customer behavior to maximize cross-sell and up-sell opportunities. For example, incorporating and analyzing dozens of data points from different systems enables firms to determine which customers are likely to respond to a premium credit card offer and least likely to default on payments. This allows firms to identify which customers to target with particular offerings and services. Similarly, firms will be able to predict which customers are at risk of churning and take appropriate corrective actions in advance to reduce churn.
Together, these capabilities will help to elevate the experience and services being offered to customers, while also helping financial services firms to create and cement a competitive edge.
Restoring trust in data
Ultimately, by adopting smart data fabrics, firms will be able to overcome the data challenges that are currently preventing them from using their data to make better decisions by leveraging a more complete and more current 360-degree view of each and every customer. With a complete and trusted 360-degree view of the customer, firms will be in a strong position to fuel new customer initiatives, enhance the customer experience by delivering cohesive and personalized interactions and offerings across departments, and set their institution apart.
The rate of innovation within the financial services sector has increased dramatically. As traditional financial services organizations attempt to keep pace, they often leverage technology from fintech providers to quickly develop or provision new cloud-based applications.
As a result, over half of fintechs globally are looking to implement new technologies that will support financial service organizations to better meet customer demand, enhance agility, and improve competitiveness.
In order to achieve these goals, financial services organizations need to be able to easily leverage and provision new fintech services and applications, and to seamlessly integrate their existing production applications and data sources with these applications.
This is where a real-time bidirectional data gateway comes in.
This webinar will discuss how a seamless connection between cloud-based fintech applications and financial services customers’ production applications and data assets will enable faster innovation to execute a wide range of business initiatives, from offering more personalized services tailored to individual customers to streamlining operations and improving compliance with regulations.
In collaboration with InterSystems, watch back on this webinar featuring:
Joe Lichtenberg, Product and Industry Marketing Director, InterSystems
Michael Hom, Head of Financial Services Solutions, InterSystems
Moderated by David Penn, Research Analyst, Finovate
The following is a sponsored post by Tim FitzGerald, EMEA Financial Services Sales Manager, InterSystems.
If the last few years have proven anything, it’s that market volatility will occur with monotonous regularity. Even if we can’t predict the exact nature of the turbulence – whether it’s the impact of geo-political events, pandemics, elections, or disasters – the effects are being felt with increasing frequency. Being able to anticipate and respond to sudden market changes has become increasingly important.
As many organizations look to obtain the capabilities needed to become more agile and resilient in the face of this ongoing volatility, the role of data has become more widely recognized. In particular, in a landscape where things can change very quickly, there is a growing understanding of the importance obtaining fresh data.
Today, the ability to see and work off data in real-time is essential for a financial services firm to compete. However, despite the clear need to be able to use fresh data, many firms face significant challenges in accessing and leveraging data in real time. At the heart of these difficulties lie the growing volume, velocity, and complexity of the data firms are dealing with.
Consequently, if firms are to become more resilient and agile to anticipate and respond to market volatility, they must begin by solving these challenges. At its core, this requires organizations to not only bridge their data silos, but also to simplify their data architecture.
The growing burden of data silos
For large numbers of financial services, siloed systems across multiple departments are proving to be a sizable burden. These ever-growing data silos lead to data that is inconsistent, disparate, and difficult to interpret. Often, these organizations have also amassed overly complex data infrastructures that rely on a disjointed set of technologies for data management, semantic layers, data pipelines, data integration, and analytics, making it difficult to obtain information and insights in a timely manner.
Together, these issues prevent firms from being able to get the insights they need to adapt to changing market conditions, capitalize on crucial business opportunities, comply with changing industry regulations, and gain an accurate understanding of risk and decisions related to financial data. Put another way, it severely impacts their agility and resiliency. Ultimately, it is far simpler for these organizations to have a system that is easy to understand, use and adapt, rather than trying to navigate hundreds of different applications dispersed across many locations.
A data architecture fit for modern-day volatility
As market volatility continues to bring these challenges into stark focus, a new architectural approach, the smart data fabric, which speeds and simplifies access to data assets across the entire business has emerged as a solution for financial services firms.
Powered by a unified data platform, the smart data fabric accesses, transforms, and harmonizes data from multiple sources, on demand, to make it usable and actionable for a wide variety of business applications. Analytics capabilities embedded within the platform, including data exploration, business intelligence, natural language processing, and machine learning, make it faster and easier for firms to gain new insights and power intelligent predictive and prescriptive services and applications.
In addition to simplifying their data architecture, implementing a unified data platform allows existing legacy applications and data to remain in place. This helps firms to maximize the value from their previous technology investments, including existing data lakes and data warehouses, without having to “rip-and-replace” any of their existing technology.
Moving forward in a volatile landscape
Faced with continued market volatility, the ability to incorporate real-time transactional data and eliminate delays in accessing data stored in production applications and data silos offers financial services firms a wide range of benefits. Not least is that business leaders will be able to make decisions based on accurate and current data, rather than data that is weeks old, helping to eliminate errors and missed business opportunities.
This consistent, accurate, real-time view of the data they need to run their business will also enable firms to make more informed and better decisions, and give them the resiliency and agility to anticipate and adapt to changing market conditions. Armed with a complete 360-degree view of both their business and their customers, financial institutions can turn their data into a true business enabler. This will empower firms to better capitalize on crucial business opportunities, comply with changing industry regulations, and gain an accurate understanding of risk and decisions related to financial data. Above all, it will ensure that they are no longer on the back foot when spikes occur and can instead continue to move their businesses forward during times of uncertainty.
Accessing and leveraging enterprise data in a timely fashion has become one of the most definitive ways to outpace the competition in the business world. In the financial services industry, financial firms must be able to use data to generate a complete view of the business and the customer at many levels of the organization.
Until recently, data-driven insights were the sole purview of leaders, stakeholders, and team members with the right technical expertise. Now, financial organizations are searching for ways to deliver insights across their organizations, from the board room to one-on-one interactions between customers and customer service representatives.
This is what’s known as data democratization, and it will be key to driving innovation in the financial services industry moving forward.
According to a recent study sponsored by InterSystems entitled “Empowering Line of Business Users Through Data Democratization,” one of the most important steps in democratizing the enterprise’s data is breaking down data siloes. The study, produced by WBR Insights and published by the Financial Information Management (FIMA) conference series, engaged 250 leaders from the financial industry to learn just how they intend to improve access to data over the next 12 months.
Data Access, Compliance, and Analytics Are Key Projects for the Future
Researchers concluded that any company that isn’t satisfied with its current ability to democratize data may need new data technologies. They may also need to consult with third-party experts to deploy enterprise-wide data governance processes and manage changes among staff members.
Indeed, 62% of the respondents said that providing improved access to siloed distributed data is among their top data priorities for the next 12 months.
Data Siloes Are the Biggest Barrier to Innovation
Innovation in the financial services industry has taken on a variety of forms. Self-service solutions for customers have become particularly attractive to organizations recently, as customers are demanding more ways to connect with their financial companies from home. Artificial intelligence and machine learning are also making inroads among financial firms due to their ability to make predictions and offer strategic insights.
But the most important asset for all these innovations is data. Without accessible and usable data, the organization can’t make use of advanced technologies or develop innovative applications for them. Too often, enterprise data is locked in silos due to systems that don’t communicate with each other.
According to the respondents to the FIMA and WBR Insights study, data siloes were among their top three biggest barriers to innovation.
Specifically, 54% of the respondents listed “data silos” as a top barrier to innovation. These organizations know that they have valuable data locked away in their systems, but because those systems can’t communicate with each other, there is effectively a barrier between the organization, its data, and the insights that data contains.
In the context of the financial services industry, it should be no surprise that unlocking the potential of that data is a top concern. Data is quickly becoming a new currency, and the ability to use customer data for insights is driving competition across the sector.
These are just a few of the insights offered by the new report by WBR Insights and FIMA. If you’d like to gain actionable insights into how you can democratize data at your organization, download the report today.
This is a sponsored post, by Michael Hom, Head of Financial Services Solutions, InterSystems. InterSystems areGold Sponsors of the upcoming FinovateEurope in London, March 22-23.
Last year was a record breaking for the global fintech sector, with investment reaching $102 billion – an annual increase of 183%. This growth was in large part spurred on by the pandemic which brought about major changes in consumer banking and spending habits, with eight in 10 people in the U.K. alone now using fintech products for banking and payments. At the same time, demand for fintech is also growing due to increased digitization among incumbent banks as these institutions try to keep pace with evolving customer demand for digital services and applications.
However, despite this growth, fintechs, much like more traditional financial services institutions, face a range of technical challenges which if not addressed could stall their progress. This was evidenced in recent research from InterSystems, which found that a staggering 81% of fintechs globally see data issues as their biggest technical challenge. Therefore, with data vital to everything from making informed decisions to delivering personalized services, addressing these challenges needs to be a priority for fintechs if they are to sustain the momentum of 2021.
The implications of fintechs’ data struggles
The data challenges being faced by fintechs fall under two distinct issues. Firstly, 41% of fintechs globally say they are unable to leverage data for analytics, machine learning (ML), and artificial intelligence (AI), while 40% of fintechs experience difficulties in connecting to customers’ applications and data systems. This indicates that not only are fintechs often unable to use their data effectively, but also they are struggling with data silos and integration.
These issues can have implications for fintechs such as hindering their ability to make informed decisions about the types of products and services they should be offering customers, and how they can continue to innovate to meet evolving customer needs. Additionally, for B2B fintechs in particular, integration challenges will make it more difficult to sell their applications to enterprise customers who need solutions that fit seamlessly within their existing infrastructure and that allow them to obtain the much-needed flow of bidirectional data.
On top of this, the data challenges cited by fintechs could hinder their ability to comply with financial regulations. Not only is this a concern from a regulatory standpoint, but it also may put the 93% of fintechs that hope to unlock the opportunities of partnering with incumbent banks at a disadvantage. After all, security and regulatory compliance are essential for banks and are key considerations when making decisions about which fintechs and firms to work with.
Time for a change of data architecture
Consequently, to build on the growth they have experienced over the last year and to be in the best position to capitalize on lucrative relationships with incumbent banks, fintechs globally must begin to address the problems with their data management. The starting point must be to find a way to bridge data silos and make integration easier.
Within the wider financial services sector, traditional firms, such as JPMorgan, Citi, and Goldman Sachs, are turning to data fabrics to solve these data challenges and provide a consistent, accurate, real-time view of data assets. A new architectural approach, data fabrics access, transform, and harmonize data from multiple sources on demand. By weaving together different data sets, from both within and outside the organization, and providing easy and uniform access to data, a smart data fabric can help fintechs to generate insights that can be used to get to know their customers better and gain complete visibility to accelerate business innovation.
This type of data architecture will also allow fintechs to create a bidirectional gateway between their applications and their enterprise customers’ production applications, legacy systems, and data silos. This approach will help those fintechs to ensure that their solutions can be quickly and easily integrated within their customers’ existing environments, which is particularly beneficial for fintechs looking to collaborate with banks.
‘Smart’ or enterprise data fabrics elevate this approach further by embedding a wide range of analytics capabilities, including data exploration, business intelligence, natural language processing, and ML directly within the fabric. This makes it faster and easier for organizations to gain new insights and power intelligent predictive and prescriptive services and applications.
As such, smart data fabrics address both the data integration challenges facing fintechs and their currently inability to use data with more advanced technologies such as AI and ML to extract valuable insights. As smart data fabrics allow existing legacy applications and data to remain in place, thereby removing the need to “rip-and-replace” any of their existing technology, this approach also enables fintechs to maximize their previous technology investments.
With so much potential within the global fintech sector, implementing a smart data fabric will allow fintechs to address their most pressing data challenges. They will have the ability to make more informed decisions based on accurate information and insights, deliver the products and services their customers need, and collaborate with other institutions. Ultimately, this will ensure fintechs are in the best possible position to make 2022 an even more successful year than the last.
“Banks are recognizing that there is a wealth of data and predicative analytics that can be used to curb future risks, but it’s all about how easily their teams can get access to it.”
Christian Lewis, Client Director of Financial Services, Unqork and Joe Lichtenberg, Global Head of Product and Industry Marketing, InterSystems, join Finovate Analyst David Penn to discuss how to cut down on latency in getting information and data to the right people, how to help organizations become more agile, and how to accomplish both goals while using fewer development resources than you might expect.
This is a sponsored post in collaboration with InterSystems, Gold Sponsors of FinovateFall
Delivering reliable, clean, timely data into the hands of decision makers is vital for financial institutions. While this has been true for quite some time, data is becoming more important than ever. The events of the past year have irrevocably demonstrated this point, with financial intuitions realizing just how powerful accurate data is when it comes to making pivotal decisions.
Adding to this complexity is the explosion in the amount of data we’re creating. You only need to revisit this mind-bending stat from TechJury to realize just how much we’re producing: “1.7MB of data (was) created every second by every person during 2020. In the last two years alone, an astonishing 90% of the world’s data has been created. 2.5 quintillion bytes of data are produced by humans every day.”
What does this increased focus on data mean for financial institutions?
The cost of managing data is only going to increase: The amount of data is growing, and with that comes growing costs associated with accessing, ingesting, processing, and storing that information. More data means more throughput and more storage, both of which you’ll pay for. And if you haven’t got systems in place to handle the increase in throughput, you’re going to experience delays. This can not only cause reputational damage, but can also have regulatory and compliance impacts if you don’t have appropriate systems in place to meet your obligations.
It’s becoming harder to compete with emerging players: There’s certainly a benefit to having an established business in that you’ve got insights at scale. With that comes the weight of managing legacy systems and architecture. The more information we pour into our systems, the harder we have to work to be agile.
Customers now expect smart insights: We’re all driven by the technology that powers our lives. And today’s customers expect financial institutions to mirror the intelligent insights that our smart watches and apps deliver to us. There’s a growing expectation that if our watches can tell us the how we can improve our health through personalized exercise goals, sleep reminders, and mindfulness breaks, then surely our banks can tell us how and when to optimize our portfolios, how to increase savings, or how to maximize lines of credit.
Data is essential for people to do their job: In the workplace there’s an expectation, particularly among those coming out of business school, that people will have access to the information they need to do their jobs. Data has become an integral part of doing business. We are rapidly moving beyond just making sure we have the data, and it’s now more about how reliable and accessible it is that makes the difference to employees.
Beyond breaking silos
There are many views on how organizations can improve movement and quality of information. However, some of these approaches can create their own issues.
Financial institutions need to move beyond breaking silos and focus on timely, clean, quality, solutions around data catalogues. This will allow them to map out the entire data needs of the organization. In short, they need to consider the connectivity of their information — how their data can be shared seamlessly across the whole data ecosystem. It’s what we refer to as “data fabric”.
What is data fabric?
Data fabric is an architecture and set of data services that provide consistent capabilities across a choice of endpoints spanning multiple on-premises and cloud environments. Gartner describes it as “frictionless access and sharing of data in a distributed network environment.”
How smart data fabric is driving agility in financial services
Implementing a smart data fabric allows financial institutions to make better use of their existing architecture because it allows their existing applications and data to remain in place. It then integrates, harmonizes and analyses the data in flight and on-demand to meet a variety of business objectives.
Having a smart data fabric allows financial institutions to remain agile in a number of ways:
Allows businesses to make smarter decisions faster
Banking is seeing new market entrants like gaming companies, retailers, transports and telcos, all clambering to get in on the financial services game. A well-constructed data fabric empowers executives and lines of business to monitor and anticipate changes, both positive and negative, in internal and external environments.
Helps identify new segment opportunities
One of our customers anticipated the impact of distressed debt amongst their credit card consumers and utilized their data fabric to proactively contact potentially affected clients. By offering extended payment terms they fostered stronger customer loyalty and mitigated a potentially large bad debt situation. This same process of customer segmentation can be used to identify new market opportunities.
Enhances customer experience
A smart data fabric allows faster processing of clean reliable data which financial institutions can use to share insights with their customers. By sharing these insights, financial institutions can foster loyalty and drive spend in a highly competitive environment.
Drives efficiency and cost savings
Finally, making decisions based on timely, accurate data allows financial institutions to reap all the benefits just described. Without the certainty that comes with reliable data, none of these decisions can be made efficiently or cost-effectively because the time and effort associated with managing data simply outweighs the benefits.
Leading financial services organizations are leveraging smart data fabrics to power a wide variety of mission-critical initiatives, from scenario planning, to modelling enterprise risk and liquidity, regulatory compliance, and wealth management.
How are businesses in financial services applying technologies like machine learning and AI? What obstacles and challenges remain for companies looking to deploy these technologies and how can these roadblocks be overcome? What does it mean for businesses to be “resilient” and why is “resilience” as important for businesses in today’s dynamic and uncertain times as “agility”?
We caught up with Jeff Fried, Director of Product Management for InterSystems, last week to address these and other critical questions for financial services companies in the COVID – and post-COVID – era. Fried was featured during our FinovateWest Digital conference last month, where he led a keynote address titled, “The 7 Steps to Using Machine Learning to Improve Your Business.”
For more insights from Jeff Fried into how businesses can make the most out of the current crisis, check out our feature Giving AI and Machine Learning the Business.
