• April
  • 2021

Choosing Analytical Tools for Digital Financial Journeys: What matters?

The digitalisation process in finance is rarely linear and intuitive, and it might be challenging to find a tool that best suits your vision. This endeavour may become even more difficult, when the underlying technology is very novel and there are no clear-cut industry standards that could assist your choice. Would it be correct then to refrain from using advanced technology until these standards develop? It might be, but the first-mover advantage could be lost. Would it be safer to sign up one of the incumbent providers? Perhaps. But you could still risk signing up to an inflexible, inferior, and overpriced alternative.

While there are not (yet) any established criteria to consider when it comes to building cutting-edge analytical capabilities within digital financial planning, we have identified a few that we frequently use to ensure efficient product development on a day-to-day basis. We believe these factors to be a useful point of reference to consider when you are planning your digitalisation journey.

Model quality and granularity

First, it pays off to examine the quality of the model and its granularity. While it might be tempting to select a tool with a very simple model at heart (such as Markowitz theory-based machines), this approach may yield an inferior service and limited opportunities to expand to future use cases. In the case of OutRank, we use a cutting-edge discrete time-series model with an ARMA-like structure and stochastic volatility calibrated to VIX historical data. This choice helped us achieve more realistic risk modelling capabilities, which is appreciated by stakeholders such as asset management or compliance teams. The model quality is also instrumental for mapping your institution’s products and incorporating house views. We also find that granularity which in our case is easily tied to fundamental assumptions results in less compliance overheads.

Performance and Scalability

The modern user experience must be fast, be it selecting goods via e-commerce websites or considering thousands of market development scenarios. In the world of financial planning, the speed of your customer journey is closely tied with cloud use. Built for the cloud means low response times, scalable performance, and affordable hosting. Leveraging cloud technologies helps OutRank to be up to 10x faster than relevant competitive offerings. It also allows our offering to scale well across different workloads. Finally, cloud-based solutions are affordable to host and represent world-class security and reliability levels, and since OutRank is completely stateless when it comes to end-customer data, privacy related compliance concerns are easily dealt with.

Ease of Use and Modularity

Based on the balance sheet approach, OutRank has the capability of providing decision-support for virtually any financial decision that would involve modelling of cash flows. This modularity provides the unique opportunity to start small with your offering (by building one journey, for instance, short-to-medium term investments) and gradually expand it to holistic financial planning capabilities. Moreover, we chose to provide OutRank as a back-end API to simplify the integration processes as much as possible. OutRank is a standard RESTful API and as such takes its input and returns its output in the JSON format, conforming to the OpenAPI 3.0 standard. Some of our partners integrated our solution in a matter of days, while Skandia, one of the largest insurers in Sweden, has performed this task in under six months.


The multi-period, simulation-based nature of OutRank translates into a more realistic and flexible representation of financial products, fees, taxes, and strategies. A multi-period Monte Carlo model delivers realistic results and the ability to analyse complex real-world situations. This characteristic allows for considering any part of or the full financial situation of a customer when decision-support is provided. The flexibility which comes out of a simulation-based, multi-period model allows for personalised solutions targeted to all sorts of customers, from retail investors to HNWIs. As OutRank supports the holistic use case, analysing assets and liabilities together, constraints in terms of adjacent systems and data sources can be efficiently navigated to minimise time-to-market for your first use cases.

Ease of Customisation

We are mindful that customisation is crucial when it comes to building your brand as a financial institution, and therefore it is important to consider whether the tool of your choice would allow you to preserve and develop the identity of your company. We created OutRank in a way that would allow the incorporation of your institution’s house views in the decision-making of your digital journeys and advisor’s projections. Moreover, it is easy to integrate bespoke risk factors, as well as bespoke risk profiling strategies, and taylor the style of the decision-support to your existing processes and ways of working.


Innovation in financial services is often stalled by compliance, and therefore it is essential to consider regulatory requirements while choosing a tool to shape your institution’s digital capabilities. The idea of OutRank stems from MiFID II transparency regulations, and therefore supports a full audit of outputs back to fundamental assumptions and configuration. Additional parts of the service include monthly maintenance of the calculation engine and its annual validation. Therefore, we ensure that our customers can trace all the outputs of the API down to the fundamental assumptions, with full methodology documentation available. This can significantly shorten compliance and NPAP processes, as well as allowing fast and easy internal adjustments due to the transparency of the model.

Similar Articles

Press Release: Max Matthiessen Partners with Kidbrooke

  • February
  • 2024
We are excited to announce our recent partnership with Max Matthiessen, one of t

The Future of Finance: Embracing Embedded Finance

  • February
  • 2024
As we progress through 2024, the concept of embedded finance, incorporating fina

A Step Towards Data Readiness: Improving Financial Data Aggregation

  • February
  • 2024
The diversity of financial data sources and forecasting requirements are common