Artificial Intelligence (AI) and Machine learning (ML) may seem both a distant and even scary prospect to some, straight out of a sci-fi movie plot, but in reality they’ve been around in our lives for quite some time now. Think Siri or Cortana, for example. A growing number of financial institutions are applying AI to their businesses via the back office and customer advice interactions to name just a couple. And, according to a report from the last World Economic Forum, 76% of banking CXOs agree that adopting AI will be critical to their organisation’s ability to differentiate in the market.

Yet, the Asset Management industry seems slow to adopt and make use of the latest AI and ML developments. Based on the report, ‘Inventing the Future of Asset Management’ that surveyed 250 Asset Management executives in North America, 95% say that AI will most certainly provide them with competitive advantages by 2025. However, when asked how far along they were in implementing AI technology, just 17% are in the advanced stage in adopting it within their organisations. 

Research suggests that in the past three years, organisations that have embraced technology have delivered 25% higher revenue and 31% higher Ebitda.

One thing seems certain; if you want to stay ahead of the game in the industry it’s essential to boost efficiency, improve analytics, and speed up processes by adopting more innovative technologies. 

Here are 5 ML and AI capabilities you can incorporate NOW:

1. Forecast and provide business intelligence

Asset Managers need real-time, accurate data to drive strategic decisions. There are many bespoke reporting solutions on the market, however the vast majority only allow for reporting on data entered into the system.

Asset Managers are increasingly demanding further reporting capability to collect and report on business metrics, such as user adoption rates, field usage and approval turnaround times. Furthermore, they need the facility to forecast growth in these areas among many others.

This pushed Fundipedia’s team to build an AI driven dashboard where users could:

  • Consume current data, but also be alerted to trends or future changes in the state of their data
  • Forecast data models based on historic data
  • Plot forecasts and trends onto charts using time-series data
  • Drill down into key metrics for more information and allow the data to be exported as PDF reports

The AI driven Fundipedia dashboard will assist Asset Managers in making data-driven decisions, spotting trends and planning for future events.

2. Route potential data problems to the right person in less time

Getting data to market vendors is quite straightforward. Understanding who to speak with from the TPA, AM or vendor when a client flags potentially inaccurate data to the vendor and/or AM is much more difficult.

The difficulty stems from the sheer amount of data and the number of touchpoints for getting that data out to the platforms. As the data moves from TPA to AM to vendor it’s touched many times, which muddies the water as to the root cause of a data issue. Did the data get sent to the vendor but they uploaded it incorrectly? Did the TPA send the wrong data and it slipped through validation? And so on…

Routing tickets manually is usually a slow, inefficient, error-prone and non-scalable process. The time between the ticket being raised and routed is lost. If a ticket is not routed to the right recipient(s), this causes further delays as the ticket is rerouted. Furthermore, it’s not uncommon for tickets to be lost altogether if they are routed too many times.

To resolve these issues, Fundipedia has built the ‘First Responder’ feature – an automated ticket routing solution that learns who is best placed to resolve issues based on their classification,  reducing the average amount of time to resolve a data issue by 78%.

3. Better understand the lineage of your data

Another struggle faced by Asset Managers is the struggle to understand the lineage of their data.  Being able to follow a piece of data from start to finish allows organisations to understand where it has come from and where it ends up. This information is vital for organisations to comply with regulatory requirements, particularly around MiFID II and Solvency II reporting.

Many firms use incomplete data models, which include data owners and descriptions etc. However they often lack the ability to store the actual journey the data takes as it flows through the organisation. 

The Fundipedia complete data management solution adds an extra tag to your data that will allow you to create your low-level attribute lineage, or high-level systems lineage, with interactive maps. 

4. Facilitating the Fund launch process

The process of launching, changing and decommissioning a new product is a highly complex one. It involves a large number of people, all of whom are usually working to a tight deadline. 

Fundiepedia has created a case management system, where Workflows can be created for a range of processes, such as the aforementioned.

Starting a Workflow, such as for the launch of a new product, generates time sensitive to-do list items for individuals, with deadline prompts and additional document population.  

As with everything else in Fundipedia, the act of being assigned and completing a to-do list item is audited and can be reported on. 

Fundipedia’s case management system comes with:

  •  A high-level dashboard through which any hold-ups can be spotted immediately
  • intelligent reporting around the time it takes to perform various Workflow process, such as to-do list items, so that the business can spot inefficiencies and take action as required
  • Intelligent prediction of the amount of time a workflow will take from start to finish

5. Third-party data reconciliation (PDF reading and data taxonomy extraction)

Regulatory rules require that Asset Managers (the regulated entities) produce documentation, and ensure all the information therein is accurate. They can, and often do, delegate the task of producing the documents to third parties (who are not regulated) but Asset Managers remain responsible for the accuracy. The FCA can act only against regulated entities. Any liability of the third party is a matter of the commercial arrangements in place between the product provider and the third party.

In short, Asset Managers are on the hook for their data being accurate not just within their business but everywhere that the data is published. To help ease the burden an automated solution can compare this data against the golden source. 

The Fundipedia data reconciliation module achieves this by taking distributed data (Factsheets, KIIDs, website data) and automatically comparing it with data located either in the Fundipedia data management platform, or from any EPT/EMT data files. This ensures that various outputs all reconcile with one another. 

The intelligent dashboard gives a high-level summary of where the data is being distributed alongside highlights of any data that does not reconcile.

AI capabilities will simplify the process of analysing, predicting and applying financial data. AI streamlines and automates repetitive processes and it is particularly efficient in its detection of, and response to, errors. 

Employing AI today will give your organisation the competitive edge it needs to survive in a highly competitive market. 

If you want to learn more about how you can utilise AI and ML in your organisation, feel free to schedule a no-obligation demo with Fundipedia today! >>Schedule a demo