For Your Interest…

Monitoring liquidity is a burdensome task undertaken at most financial firms. But advances in cloud computing, machine learning and artificial intelligence are making the process increasingly real-time and precise.

The hope is that technology will transform decades’ old and siloed liquidity systems into a single, sleek, fully-automated platform with a strong arsenal of statistical analytics for liquidity monitoring and risk management.

By 2020, it's estimated that 4.4 terabytes of data will be generated each month for every human on earth. From cell phone calls to social media, credit card purchases to satellite images, the human race is creating data at an unprecedented rate.

In every sector of society, from space travel to healthcare, corporations are scrambling to make sense of the vast amounts of data being created, and figuring out new ways to use them for boosting their business. Financial markets are no different.

In asset management, this data goes far beyond the kinds of information traditionally used by investment managers in day-to-day operations, such as market data from exchanges, regulatory filings, and earnings reports.

Cloud data warehousing is more than just a buzz word. It is the underpinning of a giant technological shift in the way all kinds of firms store, process, and analyze the ever-growing amounts of data critical to their business.

Capital markets firms have traditionally been slow to move, but outside finance, momentum is building. Here at UBTI, we have seen firsthand the surge in asset managers that want to store their data in the cloud instead of on-premise.  

Asset managers make critical data-driven decisions on every business day, unlike other industries where such decisions are made relatively less frequently.

In the case of asset management firms, when there is a change in the rating of a security, it has greater implications on its price, which requires the attention of the asset managers. This is one such example reflecting the criticality of data in asset management firms from the day-to-day operations perspective.

In our experience, serving asset management firms, we have come across many such scenarios, including monitoring and maintenance of percentage of asset allocation in portfolios, clients’ investment preferences, benchmarking performance, tracking corporate actions, maturity of bonds, expiry of futures and options, calculation of NAVs, abnormal dynamics in the price of securities, etc. All these scenarios demand instant monitoring and notifications, and timely decisions from the asset managers.

Automobiles, healthcare, entertainment, personal banking… it seems every sector in the world is undergoing rapid disruption at the hands of technology. Financial services, though often slower to adapt than Silicon Valley, is no different. A growing base of Millennials investors is forcing firms to rethink everything from portfolio construction to digital customer support.

Here’s a look at the top three fintech trends to keep an eye on this year.

Having a centralized data repository (CDR) fitted with a data quality business rules engine sets asset management firms on the path to better data management. The next step is to integrate the data between the source and target systems via the CDR. This step would result in time and money savings, reduction in user tickets and faster issue resolution (if any).

For asset management firms, the source and target systems primarily include the:

• Third-party data feeds
• Order management system
• Customer relationship management (CRM) system
• Accounting system
• Compliance solutions
• Reporting systems

The background

It’s been almost a decade since the CFA Institute unleashed the 2010 Global Investment Performance Standards (GIPS) to homogenize the way asset management firms calculate and report performance to investors. As we head into 2019, the powers that be are attempting to tie up some loose ends.

The weakness of GIPS 2010? In three words — ‘lack of scope’. The guidelines as originally written were not broad enough to cover alternative assets in the capital markets like private equity, real estate, private wealth and so on. All of this resulted in a lack of buy-in from firms who wanted to comply with the standard but whose portfolio management activities didn’t fit neatly into the categories described in the framework.

GIPS compliance has always been more of a gentlemen’s agreement than a regulatory requirement — and its success depends on voluntarily adoption. And so, the GIPS Executive Committee learned the hard way that one size doesn’t fit all.

The Centralized Data Repository (CDR) setup for asset management firms acts as the foundation for the data management initiatives. This is the first step towards achieving a single source of truth and eliminating siloed operations.

The next step is to ensure the quality of the data (that will be stored and managed in the CDR) meets the desired standards. For this purpose, data profiling is critical. Data is profiled in terms of its attributes, patterns, and functional dependencies, which in turn is analysed to create the business rules engine.

Data serves as the nerve center for asset management firms. The securities pricing data, ratings, corporate actions data, NAV etc. are mission critical for asset managers to make the right investment decisions. The compliance team, on the other hand is in need of effective data management to file compliance on time, and avoid penalties.

Most asset management firms incur costs by purchasing data from third-party vendors like Bloomberg, Factset, Rimes, Fitch etc. However, they find it extremely challenging to manage this data, and often fail to leverage it, as this data is unstructured from a holistic perspective.

In the end, asset managers are forced to analyze this unstructured (or semi-structured, in some cases) data and make investment decisions with great difficulty, while the compliance team is forced to file compliance in the nick of time.

Don’t believe the hype. Asset managers aren’t under the control of artificial intelligence (yet). But many shops currently engaged in "data science" programs are motivated by the same fear; that every other manager out there is secretly building the next-generation of robot, capable of making faster, more accurate predictions about the market than any human.