Technology is driving a digital transformation across investment management. In the future, operating a fund across borders without friction or delays; offering multiple services at low cost; and providing new services at a moment’s notice will be the price of entry. Middle and back office operations will see significant advances in speed and automation. Settlement and cash management will occur instantly, governed by rules guided by cognitive intelligence engines to instantly manage no fee, cross border payments in public ledgers that offer unprecedented transparency without compromising traditional notions of privacy, effectively eliminating today’s burdensome

reconciliation process. A common framework for compliance functions will become one of the earliest entrants, forming the basis of the most ubiquitous services in the years to come. While to date most compliance services have related to the macro economics of one specific country, there will be a gradual shift to a more homogeneous framework as countries reach consensus on what constitutes risk that should be monitored.

If this sounds like science fiction, it’s time to face reality. While this new breed of operational efficiency is still nascent, most of it is here today and adoption will soon be widespread.

The Sharing Economy, Digital Convergence and Open Standards

Today, most fund management systems are monolithic proprietary entities created and supported by a single vendor, typically running on owned or leased infrastructure. Customizing software is a complex, ongoing process requiring significant investments in resources.

Over the next few years, monolithic solutions will begin to look like dinosaurs. Newer applications will be small—designed to do one thing better than any similar application.

Today, integrating applications from different sources is complex and costly.

In the future, software will be designed with standardized open protocols that allow it to accept data from many sources in many formats.

Progressive managers will pick and choose from a variety of “single purpose” applications which work together seamlessly. These applications will be built using a common industry-standard connection and able to interact with data in a variety of formats, including Swift, XML and new formats yet to be determined.

Two of the most sought after of the new breed of service applications will be identity management and KYC to provide identity assurance in a virtual world.

Artificial intelligence will become a cornerstone of services as providers seek to process any routine quickly and cheaply while also recognizing evolving rules and incorporating those changes without programming delays.

Startups and Funding Climate

According to Forbes, more than $1 Billion has been invested in Blockchain based startups since 2008. Ethereum, T0, Enigma, Kraken, Digital tangible trust, Liquid and Accelerator are all attracting funding for new ideas based on cryptocurrency and blockchain technologies.

Security, Privacy and KYC

The current industry focus on reconciliation will disappear with the rise of public multi-entity distributed ledgers such as Blockchain, which don’t require reconciliation and offer nearly complete anonymity.

As firms pass data more freely, the potential for malware and identity theft increases significantly. This threat has given rise to new forms of cybersecurity tools that use cognitive learning, AI and natural language to quickly identify abnormal activity without relying on old-style pattern recognition. Darktrace and DeepArmor are two of this new breed.

Most of today’s privacy regulations are driven by individual governments with different regulations for sharing information, causing difficulty in abiding by KYC regulations, forcing compromises on business processes and introducing friction that impedes data velocity.

As a common business platform achieves critical mass, many of these differences will disappear in favor of uniform data sharing rules, similar to the way the adoption of the Euro led to common rules that enabled the free flow of goods.

Strong KYC applications will be one of the first prerequisites for digital transformation. Since most processing will actually be done in the cloud, the location of the systems will become less and less relevant except where it relates to government regulation of data storage.

The Impact of Distributed Ledgers and Smart Contracts on Reconciliation

Many firms outsourced reconciliation and NAV processes to offshore facilities or sub-contractors to save money and to take advantage of “follow the sun” reconciliation.

Blockchain is a sharable, public ledger that validates every cryptocurrency transaction that has ever occurred chronologically in a near instantaneous reconciliation.

Smart contracts are digital protocols to enforce or verify contract terms or performance, making many clauses self-executing or self-enforcing and giving ISDA master agreements the potential to reconcile themselves. Mundane tasks that were historically handled by banks and lawyers can be executed faster, more transparently and less expensively.

However, security, performance and programming languages are current issues with Blockchain and smart contracts. As chains grow ever longer, calculating smart contract values in real time may cause performance issues.

Improving performance is the concept behind startups such as Blockstream Liquid, which moves transactions off the primary chain to improve performance.

Since smart contract platforms run across multiple physical systems, errors and bugs are quickly visible but not quick to fix, another potential issue. Security holes become easier to exploit, as seen in the spectacular attack on the distributed autonomous organization widely known as the DAO.

The Rise of Cryptocurrency

Cryptocurrency may make currency trading both anachronistic and superfluous. Bitcoin, Ethereum Ether, and Codius Ripple are the most well-known of the cryptocurrency cadre, but there are more than 700 cryptocurrencies in existence. In addition to those three, cryptocurrencies with market caps over $20 million include Litecoin, Monero, Ethereum Classic, Steem, Dash, NEM, MaidSafeCoin, Factom, Lisk, DogeCoin, Dixiedao and Nxt, according to Coinmarketcap.

Artificial Intelligence and Cognitive Robotics

Today, middle-and front office systems run on a series of configured or hard coded rules adapted to fit the fund’s daily operations. Any rule changes must be made using the same technique.

Soon, an AI engine will monitor transactions, watching for alternatives that might have provided a better outcome or instances when a human overrides the default action. It will suggest adding or changing its own rules to improve outcomes or more closely align with actions taken by humans. After human approval, the AI engine will modify the rules on its own without the need to wait for IT to make changes.

Public Cloud and SaaS

IT infrastructure typically costs about the same whether on premise or in the cloud, but the SaaS model adds the ability to scale infrastructure and computing power to match increases or decreases in trading volume, allowing funds to pay only for the capacity they use in a specific period.

Contrast that with the current on premise model, where funds must invest in sufficient IT capacity to manage their highest trading volumes. Most of this capacity sits unused between peak loads, making the capital investment in infrastructure higher than necessary.

The cloud also provides an unmatched degree of security. Most cloud providers have taken the time to get SSAE16 Soc I and II certified, ensuring that their processes and procedures meet rigorous standards, and providing physical and biometric security such as CCTV or live guards.

In-Memory Databases

Traditional relational databases are inflexible, slow to process, difficult to modify and do not lend themselves to data mining or rapid simulations.

Rather than store data persistently in predefined layouts, in-memory databases keep all the data in memory, eliminating the constant input/output to hard drives that slows down relational processing. In-memory databases do not require fixed file layouts since they can create structures on the fly as needed, making them ideal for simulations and data mining.

An in-memory database can accept large volumes of data in both structured and unstructured formats, combining disparate data easily to search for patterns or trends. Using an in-memory database with a big data analytics engine enables companies to use large volumes of data in a variety of different formats at an extremely high velocity to generate predictions and insights that would be impossible with traditional reporting tools.

Because an in-memory database uses a great deal of memory while processing, it is an ideal solution for the cloud where companies pay for the infrastructure they consume rather than owning capacity in anticipation of peak loads.

In addition, as more governments dictate where citizens’ data can be stored, in-memory data bases become even more valuable. When data is needed for real time processing, the in-memory database can request it and use it for analysis. When the analysis is finished, the data is automatically purged from memory, eliminating any regulatory concern about where the data resides.

The Future is Here

The concurrent rise of these complementary technologies is creating massive changes in fund management. While some of these predictions may sound futuristic, many of them are here now. All will be “business as usual” within the next three to five years. Fund managers will need to move quickly to adopt these new digital business models as soon as they become commercially viable or be swept away by the competition.

To contact the authors:

K B Venkataramanan, Chief Information Officer at Viteos Fund Services: [email protected] 

David A. A. Ross, Global Head of Marketing at Viteos Fund Services: [email protected]