A change of focus
18 Sep 2019
The fund industry finds itself with the ability to shift focus away from the resource-intensive burden of implementing regulations as it looks to ramp up innovation, according to Arnaud Misset of CACEIS
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Now that the incessant barrage of new regulations is slowing, the fund industry finds itself with the ability to shift focus away from the resource-intensive burden of implementing regulations and look to ramping up innovation and improving efficiency. Although companies have done their utmost to maintain innovation during recent years, business efficiency has often taken a back seat due to the entangled complexity of new regulatory processes taking up IT development time. Today, however, companies within the fund industry, which is an increasingly competitive environment with ever-growing data volumes, are keen to understand the opportunities in terms of efficiency that present themselves, and one key element in the search for efficiency gains is robotic process automation (RPA).
Simply put, RPA is computer software that can use IT systems designed for humans. Clearly no automations here, but instead, software that can operate with extensions such as optical character recognition (OCR) to read handwritten entries such as in forms, natural language processing (NLP) to extract data from unstructured text, chatbots to respond to standard queries and possibly artificial intelligence (through links with Google or IBM’s Watson platform for business). RPA’s ability to perform data entry, and use plug-ins to understand and respond to client queries, makes it analogous to offshoring, and in some ways it is, but it has some key differences.
CACEIS itself has undertaken very little offshoring, as the potential loss of control (through dealing with a far-off provider with cultural differences and being just one of a number of clients) is a major factor that explains the reticence to pursue that avenue. RPA, on the other hand, enables companies to maintain control of their business, especially if they create the competence centre internally rather than rely on an external provider that may itself become a source of issues due to the lack of required industry expertise. Many companies see this RPA technology as a key component of the future financial ecosystem and therefore decide to make the necessary investments internally.
We often hear it said that RPA’s principal objectives are nothing more than the ruthless pursuit of pure efficiency, however, the reality is different—and not just marketing “spin”. The back-office, which is always the first area to go under the RPA spotlight, is where we find a glut of tedious, repetitive processes that are the source of a high level of operational risk and have low added value. RPA’s prime objectives in such an area are to reduce operational risk stemming from human error, improve client experience by increasing service quality and professionalism, and free up staff for tasks where human intervention is key, such as client contact and handling more complex transactions.
As above, most RPA projects start in the back office, however, prioritisation of the processes is an essential task both in terms of the project’s efficiency (picking the low hanging fruit first as opposed to simply branching out) and in terms of its image within the company, (targeting the tedious, repetitive processes where there is no strong feeling of ownership). Here, RPA goes hand-in-hand with a lean management approach, which promotes continuous efficiency and quality improvement through small incremental changes over time. It is important to note that, whenever RPA is implemented on an isolated process, the impact on upstream and downstream processes must be carefully considered.
Later on, as a patchwork of isolated tasks taken over by RPA start to interface with each other, then efficiency levels can further improve. On average, RPA increases business efficiency by some 10 to 12 percent for individual tasks, but this figure can rise significantly when several processes are daisy-chained, end-to-end.
The final point that should never be underestimated in any RPA project is communication. There is a lot of fear surrounding the topic, and it’s not limited to operational department employees. Management, IT, compliance, risk, legal, HR and other areas may be impacted by RPA, so together with operations also need a structured communication campaign to explain the technology, the benefits and goals in order to get them on-side. Liaising with staff delegations and trades unions are also a key part to ensure smooth implementation of what is a very promising technological innovation.
The other key topic for asset servicing companies is data analytics. Asset servicing companies handle huge volumes of data, and today are leveraging big data technologies to transform it into valuable business insight services.
Data lakes, the term for the massive pools of information generated by funds’ day-to-day activities, are the defining element of big data technology. However, the quality of this data is the foundation of the entire activity and much care must be taken to ensure that all sources of data are reliable in order to guarantee accurate results and generate a genuine data warehouse of standardised data.
Data lakes are not only populated from information generated within the asset servicer, but is also sourced from other service providers, fund managers themselves, external data providers, information on social networks, as well as economic indicators or any other information that may be relevant. Data lakes generally hold up to several years’ worth of historical data and are fed with information as close to real time as possible, which enables institutional investors and fund managers to query up-to-the-minute data.
Financial reporting
Data analytics can be deployed in many areas to assist institutional investors and fund managers, but a key area is financial reporting. The financial industry is particularly concerned by the exponential growth in data to be managed. In the wake of new financial reporting regulations, in particular the second Markets in Financial Instruments Directive (MiFID II), Basel III, Solvency II and the Alternative Investment Fund Managers Directive (AIFMD), institutional investors and investment management companies have had to confront massive data management and analysis tasks in order to meet transparency requirements in terms of information and reporting to the relevant national authorities. Big data technology’s capabilities enable them to quickly and realiably make queries and generate accurate reports on look-through, performance and risk, and regulatory ratios, permitting them to focus on their core business of generating investor value.
Fund distribution
Data analytics can also be used to provide reference indicators for investment management companies in order to refine their sales strategy. For example, ex-post analysis of subscriptions and redemptions will allow the determination of a correlation between investor behaviour and a fund’s performance relative to its benchmark. This information can be of valuable assistance in establishing a fund’s commercial positioning. Such reports will enable investment management companies to achieve more in-depth marketing analysis (inflows, investor behaviour, distribution network). In addition, analysis can distinguish by type of final investor, country and distributor, permitting investment management companies to better determine their target clients and the optimal distribution network.
Investor behaviour analysis gives investment managers even deeper insight into their customers’ actions. Utilising data from social networks such as Twitter and Facebook, data analytics tools can provide answers to soft questions on subjects such as brand visibility, both of the investment manager’s own brand and that of its competitors. The technology also enables sentiment analysis that helps asset managers answer questions such as ‘how are social networks talking about the brand?’ and ‘what is the resulting impact on order collection?’. Such insight becomes increasingly important as the younger, more internet-savvy population mature and become potential investors, who are keen to use the internet to seek information on and discuss investment opportunities via online platforms.
KPI analytics
Along with financial reporting and fund distribution analysis, data analytics provides another advantage for those investment managers working with an asset servicing provider. They can generate key performance indicators (KPIs) on the provider’s performance, allowing them to view statistics on net asset value calculation performance and settlement performance in real time. The flexibility of the open platform enables managers to query the data in so many ways that the information can be precisely tailored to the manager’s needs, and the results are generated and displayed in clear and accurate reports within seconds.
The powerful tools that enable data analytics on big data use vast amounts of processing power locally, but can be securely accessed through a flexible web interface or even applications designed for mobile devices. This allows financial reporting, fund distribution and KPI analytics to be handled around the clock, no matter where in the world you may be. Furthermore, the data lakes, analytics algorithms, and the systems it runs on are constantly growing more powerful, which means the possibility for insight opportunities and new services are constantly evolving. To stay ahead of the competition and remain at the forefront of technology, data analytics and RPA are key.
Simply put, RPA is computer software that can use IT systems designed for humans. Clearly no automations here, but instead, software that can operate with extensions such as optical character recognition (OCR) to read handwritten entries such as in forms, natural language processing (NLP) to extract data from unstructured text, chatbots to respond to standard queries and possibly artificial intelligence (through links with Google or IBM’s Watson platform for business). RPA’s ability to perform data entry, and use plug-ins to understand and respond to client queries, makes it analogous to offshoring, and in some ways it is, but it has some key differences.
CACEIS itself has undertaken very little offshoring, as the potential loss of control (through dealing with a far-off provider with cultural differences and being just one of a number of clients) is a major factor that explains the reticence to pursue that avenue. RPA, on the other hand, enables companies to maintain control of their business, especially if they create the competence centre internally rather than rely on an external provider that may itself become a source of issues due to the lack of required industry expertise. Many companies see this RPA technology as a key component of the future financial ecosystem and therefore decide to make the necessary investments internally.
We often hear it said that RPA’s principal objectives are nothing more than the ruthless pursuit of pure efficiency, however, the reality is different—and not just marketing “spin”. The back-office, which is always the first area to go under the RPA spotlight, is where we find a glut of tedious, repetitive processes that are the source of a high level of operational risk and have low added value. RPA’s prime objectives in such an area are to reduce operational risk stemming from human error, improve client experience by increasing service quality and professionalism, and free up staff for tasks where human intervention is key, such as client contact and handling more complex transactions.
As above, most RPA projects start in the back office, however, prioritisation of the processes is an essential task both in terms of the project’s efficiency (picking the low hanging fruit first as opposed to simply branching out) and in terms of its image within the company, (targeting the tedious, repetitive processes where there is no strong feeling of ownership). Here, RPA goes hand-in-hand with a lean management approach, which promotes continuous efficiency and quality improvement through small incremental changes over time. It is important to note that, whenever RPA is implemented on an isolated process, the impact on upstream and downstream processes must be carefully considered.
Later on, as a patchwork of isolated tasks taken over by RPA start to interface with each other, then efficiency levels can further improve. On average, RPA increases business efficiency by some 10 to 12 percent for individual tasks, but this figure can rise significantly when several processes are daisy-chained, end-to-end.
The final point that should never be underestimated in any RPA project is communication. There is a lot of fear surrounding the topic, and it’s not limited to operational department employees. Management, IT, compliance, risk, legal, HR and other areas may be impacted by RPA, so together with operations also need a structured communication campaign to explain the technology, the benefits and goals in order to get them on-side. Liaising with staff delegations and trades unions are also a key part to ensure smooth implementation of what is a very promising technological innovation.
The other key topic for asset servicing companies is data analytics. Asset servicing companies handle huge volumes of data, and today are leveraging big data technologies to transform it into valuable business insight services.
Data lakes, the term for the massive pools of information generated by funds’ day-to-day activities, are the defining element of big data technology. However, the quality of this data is the foundation of the entire activity and much care must be taken to ensure that all sources of data are reliable in order to guarantee accurate results and generate a genuine data warehouse of standardised data.
Data lakes are not only populated from information generated within the asset servicer, but is also sourced from other service providers, fund managers themselves, external data providers, information on social networks, as well as economic indicators or any other information that may be relevant. Data lakes generally hold up to several years’ worth of historical data and are fed with information as close to real time as possible, which enables institutional investors and fund managers to query up-to-the-minute data.
Financial reporting
Data analytics can be deployed in many areas to assist institutional investors and fund managers, but a key area is financial reporting. The financial industry is particularly concerned by the exponential growth in data to be managed. In the wake of new financial reporting regulations, in particular the second Markets in Financial Instruments Directive (MiFID II), Basel III, Solvency II and the Alternative Investment Fund Managers Directive (AIFMD), institutional investors and investment management companies have had to confront massive data management and analysis tasks in order to meet transparency requirements in terms of information and reporting to the relevant national authorities. Big data technology’s capabilities enable them to quickly and realiably make queries and generate accurate reports on look-through, performance and risk, and regulatory ratios, permitting them to focus on their core business of generating investor value.
Fund distribution
Data analytics can also be used to provide reference indicators for investment management companies in order to refine their sales strategy. For example, ex-post analysis of subscriptions and redemptions will allow the determination of a correlation between investor behaviour and a fund’s performance relative to its benchmark. This information can be of valuable assistance in establishing a fund’s commercial positioning. Such reports will enable investment management companies to achieve more in-depth marketing analysis (inflows, investor behaviour, distribution network). In addition, analysis can distinguish by type of final investor, country and distributor, permitting investment management companies to better determine their target clients and the optimal distribution network.
Investor behaviour analysis gives investment managers even deeper insight into their customers’ actions. Utilising data from social networks such as Twitter and Facebook, data analytics tools can provide answers to soft questions on subjects such as brand visibility, both of the investment manager’s own brand and that of its competitors. The technology also enables sentiment analysis that helps asset managers answer questions such as ‘how are social networks talking about the brand?’ and ‘what is the resulting impact on order collection?’. Such insight becomes increasingly important as the younger, more internet-savvy population mature and become potential investors, who are keen to use the internet to seek information on and discuss investment opportunities via online platforms.
KPI analytics
Along with financial reporting and fund distribution analysis, data analytics provides another advantage for those investment managers working with an asset servicing provider. They can generate key performance indicators (KPIs) on the provider’s performance, allowing them to view statistics on net asset value calculation performance and settlement performance in real time. The flexibility of the open platform enables managers to query the data in so many ways that the information can be precisely tailored to the manager’s needs, and the results are generated and displayed in clear and accurate reports within seconds.
The powerful tools that enable data analytics on big data use vast amounts of processing power locally, but can be securely accessed through a flexible web interface or even applications designed for mobile devices. This allows financial reporting, fund distribution and KPI analytics to be handled around the clock, no matter where in the world you may be. Furthermore, the data lakes, analytics algorithms, and the systems it runs on are constantly growing more powerful, which means the possibility for insight opportunities and new services are constantly evolving. To stay ahead of the competition and remain at the forefront of technology, data analytics and RPA are key.
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