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Powerful technologies are reshaping the asset management landscape

The effects of blockchain, big data, machine learning, and other exponential technologies are rippling across the financial service industry, offering innovative methods to reduce operational inefficiencies and boost growth. 

Representatives from financial services firms in Canada, the US, and around the world discussed this wave of transformation at the KPMG Asset Management Forum 2017. The forum included a panel on technology innovation in the asset management industry and featured Jennifer Stott, Senior Vice President, I&TS Technology, Technology & Operations, Royal Bank of Canada. The discussion focused on the following key themes: 

  • the accelerating use of data analytics in the financial services industry
  • prospects for distributed ledger technology, such as blockchain
  • limitations of “big data" in innovating solutions

Data analytics now “part of every conversation"

Data is “the new precious metal" and data analytics is the best way to mine it, said Stott. Data analytics is the process of reviewing data sets in order to generate insights about the information they contain, typically using specialized systems and software.

Data is “the new precious metal" and data analytics is the best way to mine it

Increasingly, data analytics technologies and techniques are being used to integrate external, unstructured data with internal, structured data to develop new insights, inform decision-making, and test specific hypotheses. One example is machine learning, where computer programs analyze data to look for patterns in client activity in order to anticipate the next expected action. For example, if an action in a stream of activity does not match the typical pattern, this may be an indication of fraud, commented Stott. The use of machine learning allows these patterns to be identified far more quickly and accurately than using manual, human-led techniques; thus increasing the both the efficiency and effectiveness of fraud detection, and protecting customers and financial institutions from its impacts. 

Panellists also discussed the nuances between approaching machine learning as a tool for "intelligent automation," versus for "automatic intelligence." This is the fine distinction between machine learning used to derive insight that can be actionable by humans (automated intelligence), versus the case in which machine learning exposes opportunities to trigger automated actions (intelligent automation). 

Another example of the growing use of data analytics in financial services combines publicly-available data, such as information from Twitter feeds and stock prices, with internal banking data to look for insights. Using automated intelligence technologies, asset managers can monitor how markets might react to events with multiple outcomes, such as elections. This type of "sentiment analysis" can be leveraged to predict trading patterns and investment behaviours. “If a picture paints a thousand words," comments Stott, “then a picture drawn from thousands or millions of data points is even more impactful." 

The role of distributed ledger technologies

Key insights

  • While new technology is advancing rapidly, the importance of human insight is not diminishing and may grow as the capabilities of data analytics, using "automated intelligence", continues to expand
  • The application of technology solutions, such as blockchain, should appropriately match the business imperative

Panel participants also reviewed the growing role of distributed ledger technologies such as blockchain. Originally created to enable the anonymous exchange of digital assets, including bitcoin, blockchain technology is now appearing in a variety of commercial applications. These include, for example, adding blockchain technology to credit cards to allow customers to engage in secure transactions, whether using alternative currencies such as bitcoin, or traditional currencies such as dollars and euros. 

The current challenge, panelists suggested, is in finding ways to effectively monetize blockchain technologies. In discussing the prospects of blockchain technology to impact the banking and financial services sector, Stott noted that the features of distributed ledger technology may be "overkill" for some aspects of the financial services industry. For example, distributed ledger technologies create a permanent record of each transaction. While this aspect of blockchain is often touted as a core benefit, it may run counter to existing regulatory requirements for the retention of client information, which typically specify that client records may be kept for a defined period of time, but not permanently. For Stott, some uses of blockchain as a recordkeeping technology are potentially excessive, and it is important to appropriately match the technology solution to the business imperative.

The existing SWIFT system today functions as a trusted and closed network for recording and verifying financial transactions

Stott also noted that the existing SWIFT system today functions as a trusted and closed network for recording and verifying financial transactions. While blockchain has the capacity to disrupt the SWIFT system, the benefits of disruption are not always immediately clear, says Stott. The SWIFT system already delivers the benefit promised by blockchain, and does so in a way that meets current regulatory standards and practices. 

Limitations on the prospects for big data

Finally, panel participants discussed both the prospects for and the limitations of big data and exponential technologies to make meaningful predictions about future events. The term 'big data' is used to describe extremely large data sets that can be analyzed computationally to reveal patterns and trends, particularly relating to human behavior and interactions. 

There are limitations. Stott commented that some events, such as election outcomes, are essentially unforeseeable, despite increasing data sets and expanding computational power, as “humans are inherently unpredictable." With advanced data analytics applied to ever-growing data sets, the human factor will always be present when considering behaviours, preferences, and outcomes. Looking forward, the financial services industry should expect new technologies to bring new capabilities to bear, but the role of human insight is unlikely to be rendered obsolete anytime soon.

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  1. KPMG (September 26, 2017) Asset Management Forum 2017