Defining real innovation in technology

Defining real innovation in technology

Innovation in FinTech is a hot topic right now; there is a torrent of articles, blogs and white papers being published every day, each professing a deep understanding of how to innovate in this space.

 Apparently, everyone is either an expert or possesses a crystal ball!

 Those in immediate need of services are struggling to sort through the analyses of opinion makers and separate the hype from true leadership and innovation.

 They should beware: many established vendors are dressing up their tired old products and services in hipster garb in order to claim that they are ‘innovating’. 

 They claim to be building utilities too, however these are simply off-shoring projects, using the same old-fashioned software and expensive staff.

 The world has changed significantly since we launched the Duco Cube service in April 2013: “best of breed” hosted services have gained traction and are helping firms across the finance industry to cut costs, cope with regulation and simplify business processes.

 True innovation is about bringing fundamental research from the university lab directly to operations in industry.

 When we decided to build a data reconciliation platform, the decision was shaped by the painful experience of trying to apply existing solutions to a wide range of data.

 The existing tools were built to solve very specific problems, for example reconcile bank balances, match FX trades, perform depot reconciliations, etc.

 While they can be good for dealing with one kind of data, they tend to be poor at solving the wider problem of establishing data controls across an entire organisation.

These tools also require substantial effort and resources to deploy and maintain, and are completely intimidating to non-technical users.

 Today, it is clear that the rise of consumer technology is heightening expectations that all members of staff should have access to powerful and usable technology. 

 When you deliver this technology via a self-service model, you also empower your employees to help themselves, which is highly cost-effective and increases efficiency.

 Using these tools, you can put the power to establish these data controls in the hands of business users who need them most, and enable the business to cope with a wide array of different types of data, all on one platform.

 It turns out, however, that bringing consumer-like levels of simplicity and sophistication to the enterprise is incredibly challenging.

 The slick consumer apps we use every day focus on simple business models. By contrast, the existing infrastructure, complexity of products and regulatory pressures in the finance industry conspire against this kind of simplicity.

 The data you wish to reconcile is rarely in the same format. The traditional response to this is to employ complex IT tools to transform it, but these are both expensive and unusable by most business users.

 We created the Natural Rule Language, which traces its roots back to Computer Science Ph.D. research.

 This language enables business users to write straightforward rules that manipulate data, using English sentences.

 This makes it easy to overcome differences in data format, removing the need to use IT tools entirely. Since they are effectively English, the rules are clear to everyone, and they are easy to maintain.

 It often takes many months to see the first results when you use traditional reconciliation tools. To control implementation risk, companies resort to processes demanding lengthy business requirements gathering, multiple iterations and long testing periods.

 Business users are often involved only right at the beginning and the end of such projects, while much can go wrong in the middle.

 This problem can be overcome by using a self-optimising algorithm capable of matching very large sets of random data quickly and accurately, in memory.

 This empowers users to take a much more iterative approach to reconciliation projects, as they get initial results immediately and can quickly fine-tune the matching rules in small increments.

 Such an algorithm can learn the form of the data and optimise itself as it works, allowing users to tackle very large data sets (e.g. millions of trades) and deliver meaningful insights in hours or days, rather than months.

Many systems dealing with complex data are themselves complex. There are too many buttons on every screen, making the user experience unpleasant, cumbersome and confusing.

 The challenge therefore is to make applications modern and simple, take advantage of the latest web technologies, be responsive and scale to mobile devices. After all, nobody expects to read a manual anymore!

 To make things clear, we rely on strong visual metaphors, such as our “time machine” for seeing how results change over time or “change tracking” in process configuration, making it easy to spot differences.

 This leads to an intuitive experience for all users. To bring a true consumer-like experience to FinTech, you must produce a product that aligns itself with the way we use the internet, our smartphones and our laptops.

 The world is not a static place. Customers need the ability to react to the market, so they need their partners to be able to deliver value quickly.  Hosted services have the ability to deliver this value on an on-going basis, without disrupting a client’s operations.

 We were fortunate to start with a clean slate, which means we don’t have to dress up old ‘cash cow’ products in order to be perceived as being innovative.