The following is a sponsored post from InterSystems, Gold Sponsors of FinovateWest Digital, November 23 through 25, 2020.

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In an increasingly digital world filled with chatbots, tap-and-go payments, and “buy now, pay later” credit lines, hyper-personalization is the new frontier on top of a new frontier in financial services.

What is hyper-personalization?

Hyper-personalization enables financial services organizations to leverage the huge volumes of customer data they have in their systems efficiently and effectively to make more specific and more relevant product recommendations, such as an increase of a credit limit at the point of sale, or a list of previous interactions pushed to the chatbot, allowing it to pick up where it last left off. It does so by analyzing the data available to it through the power of analytics, artificial intelligence (AI), and machine learning.

It offers immense growth opportunities for all financial services providers if they can cater to small and specific groups. Hyper-personalization can foster loyalty in an era in which loyalty has declined, and it pushes the next generation of consumers and investors towards those financial services which can be agile in what they offer.

Traditional firms and hyper-personalization

Traditional firms are often encumbered by processes built up over decades. These processes are ingrained and necessary for them to have operated the way they have successfully and for so long.

To these firms, those same processes hinder the uptake of advances such as AI, data analytics and machine learning.

Yet these and other new technologies do not require traditional firms to re-imagine how processes work, nor does implementing have to be as obtrusive and disruptive as a full digital transformation initiative, for example. Rather, technology can be implemented in the background and effectively manage itself, be installed quickly and efficiently in existing systems without disrupting the rest of the business. Some can even run adjacently to everything else the business does.

Traditional firms have decades or more worth of data. Analytics tools, AI, and machine learning work together to make sense of it all, wherever it might be and in whatever language it might be in, and surface actionable insights from all of it. Importantly, these technologies work in the background, without disrupting any mission-critical processes.

How can traditional firms hyper-personalize?

1. Traditional firms can deploy a smart data fabric, which is effectively a layer which sits above all of the firm’s available endpoints and distributed services — whether it be in the cloud, on-premise or both — and ensures those endpoints and their capabilities speak the same language.
2. Next, the data needs to be put through proper governance procedures to ensure it is clean, relevant and has the necessary integrity to be used with confidence for the right reasons by the organization — it needs to be accurate, reliable, complete, appropriate, and credible. For this to occur, it goes through something of a digital centrifuge which analyses its health and cleans it before having it ready for primetime.
3. Once this is done, the rich streams of data inherent across the company can be mined, analyzed, and surfaced using the power of AI and machine learning.

This may sound like a lot of steps and go against the grain of what we’ve been discussing in this article. But rest assured, all of these technologies can be implemented with little to no disruption to operations, and they work in the background while delivering key insights for the data almost in real-time. It’s through using these technologies that traditional firms can, at last, unlock those rich and extensive streams of historical data dating back decades, which in turn provides a clear method to fostering loyalty. Research shows that customers want a hyper-personalized experience. According to Accenture, 91 percent of consumers are more likely to shop with brands who recognize them, remember them, and provide them with relevant offers and recommendations.

Conclusion

Traditional firms have a hyper-personalization advantage thanks to possessing a trove of legacy data and brand recognition. They just need to embrace what is available to help leverage their data and analytics to get them to their intelligent future — and trust that it can and will co-exist with existing processes.

If they allow technology to do the heavy lifting for them alongside their existing processes, traditional firms will be able to leverage decades of data to their advantage and engage in new ways with customers, without having to re-invent the wheel.

The following is a sponsored post from Michael Hom, Head of Financial Solutions at InterSystems, Gold Sponsors of FinovateFall Digital 2020, September 14 through 18, 2020.

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Currently, external factors like the COVID-19 pandemic mean that the global economy has become increasingly volatile and capital markets firms are having to work harder than ever to make sure users, both retail and institutional, can continue to trade without interruption.

As these financial organizations look to mitigate risk in this period of uncertainty, gaining operational resilience, implementing risk mitigation strategies, and having the right technology in place will be crucial to continue to deliver value to customers, comply with regulations, get ahead of the competition – and, most importantly, maintain trust.

Given this, the pressure for incumbents to upgrade infrastructure is only increasing, but challenges remain in doing so. While the pandemic may have been the linchpin for organizations to start embracing new technologies there are still barriers to overcome and best practices to be put into play to not only mitigate risk, but also prepare capital markets for what’s to come in the future:

Replacing legacy technology

Critical to mitigating risk is ensuring data is available quickly and easily accessible. For many capital markets firms this is an area where they struggle due to a significant amount of legacy technology in their infrastructure and, consequently, data siloes.

Connecting these disparate systems will be vital to not only help them with performance issues they have today, adapting to situations such as mass remote working, for example, but also so they are capable of growing with them into the future.

This requires them to adopt solutions that can seamlessly run, scale, and expand into the cloud. By replacing legacy infrastructure, they will have the benefit of providing new technologies and innovations access to their wealth of valuable data.

These solutions should also be location agnostic to allow capital markets firms to be agile and take advantage of new technology and services and bring that into their existing infrastructure.

Investment in the future

As these institutions look to replace their legacy technology, they should focus their investments on two key areas.

First, they should invest in platform scalability as being able to scale up as the market spikes is crucial and can be a major differentiator. This scalability can even give firms a competitive edge with some firms having recently gained market share solely due their ability to scale up.

The second area of investment should be in analytics and automation that can support and, in some cases, reduce the manual-intensive workload. We’ve already seen increases in algorithmic trading and customer chatbot technologies, while many organizations within the financial services industry use AI to automate processes, such as fraud checks and compliance.

With less time spent on time-intensive manual tasks, capital markets firms will be able to direct their attention to more value-adding services for their clients. The use of AI will help to spot patterns and anomalies in those patterns much faster for fraud prevention, while also reducing the risk of human error.

Gaining access to real-time data

Within capital markets firms, there is a growing requirement to be able to access real-time data so these organizations can simplify their stack and get access to transactions that are happening in the moment. This will allow them to produce more time-sensitive reporting so they can make appropriate business decisions and better comply with regulatory requirements.

Data fabric

Data fabrics are fast becoming a key trend within data management across the board, helping to reduce friction. Improving the accuracy, availability and accessibility of data and should also be a consideration as capital markets weather this period of uncertainty and beyond.

A data fabric that uses the latest technology will help organizations to better grasp data governance, ensure that their data is clean and accurate, to harmonize that data where appropriate, and make it more accessible. All of these will help them derive more value and better insights from their data to help drive their enterprises and those of their customers forward.

How can capital markets firms not only survive, but also thrive?

As capital markets firms look beyond this period of volatility to thriving long term, it’s vital they embrace agility by implementing modern technology with a focus on analytics and automation. This will allow them to quickly adapt to changing and new business needs by helping them to make use of their data, analyze it, monetize it, and turn it into actionable intelligence.

In an increasingly competitive landscape, where new market entrants aren’t weighed down by legacy technology and architectures, this will be a key differentiator and enable capital markets firms to take advantage of new opportunities within the market faster.

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If you want to hear more about this subject, listen to this webinar in which InterSystems takes a deep dive into the challenges facing capital markets firms and how they can mitigate risk, alongside a panel of other industry experts from Northern Trust, Westwood Group, and SIX Securities & Exchanges. Or read InterSystems‘ latest blog posts on Data Excellence.

The following is a sponsored blog post by Chris Papathanassi, Global Solution Lead, Lending with Finastra. Papathanassi discusses the two challenges facing lenders: data quality and ensuring a true “golden source” and leveraging real value through data connections. Find out more in the full report >>

Today, digital is the only way to do business. But even though everything they do can be expressed in ones and zeros, most financial service organizations simply aren’t set up to be truly digital. In the context of the current disrupted, volatile and remote-working global economy, doing digital brilliantly is now a matter of survival and urgency for many financial firms – no longer simply a ‘nice to have’. 

Digital transformation is difficult for even the simplest business models, and in lending in particular, there is a real challenge. When it can take up to three months to get cash out of the door, it’s hard to see how any bank can keep up with the digital shift. There is a continued dependency in lending on paper documentation and face-to-face contact.

Despite this, the challenges of digitalization are more than balanced out by the potential benefits. You’re likely aware of a few of these already: 

• Increased efficiency – removing repetitive, non-value-added work and moving towards real-time processing 
• Personalization – delivering relevant customer service even in a socially-distanced context 
• Improved credit management – providing integrated, rules-based systems for greater decision speed and transparency 
• Proactive risk management – using APIs and platforms to “join up” the risk and sales processes
• Self-service for corporates – providing a digital channel that empowers corporate customers 
• Unlocking the value of data – bringing data together from disparate sources so its true value as a commodity can be leveraged

So, what needs to happen for lending to get there? 

One of the key issues is data quality and the “golden source”.  The bespoke nature of lending makes it hard to maintain data quality and consistency. Lenders have their own individual nuances and conventions. And corporate borrowers that have lending relationships with many different organizations will download and manipulate data so it’s in a format they can work with.

As one major bank asked us: “How can we ensure what the source of truth is across different applications?”

What’s more, as data moves through different systems in a digitalized and connected world, it changes too.  

This points to the second challenge, which is that digitalized lending data is only valuable when it can be connected to the other pieces of the puzzle, to provide the big picture lenders and borrowers need. Right now, firms are still downloading data into Excel, manipulating it, and re-sending it.

Digitalization plus API capabilities, however, makes it possible for stakeholders to see the same pieces of data in the same state. It’s this connectivity that is key to realizing the full benefits of digitalization and addressing the “source of truth” issue. 

Digitalization also opens data to new technologies such as AI, machine learning, and robotic process automation, which can create new efficiencies and value for banks and customers. And for processes such as syndicated lending that have multiple players, it can be combined with cloud technology to enable more collaboration and better access to a single source of truth.

APIs in the cloud can make innovation more accessible to banks, overcoming the challenges of integrating in-house and external products. In essence, on platforms, banks have access to pre-integrated, interoperable solutions and better access to the broader financial services ecosystem, where they can explore innovations and consume them at speed. 

This potentially changes the shape of the lending industry, opening up interesting questions. What do banks want to be? Leaders in the lending business or providers of specialist products? With digitalization both options are possible, creating an opportunity for lenders to add value and build their lending businesses – or to disintermediate healthily. 

Interested in learning more? Download Finastra’s new report, Lending in the New Normal >>