Driving stronger front office performance with AI/ML services

​​​Outsourcing services have helped fuel unprecedented growth and diversification in asset management. New technologies are driving next-generation outsourcing. Increasingly, third-party risk and research services providers are deploying AI/ML while passing on the benefits of accuracy, scale, and efficiency to their clients.

For asset managers, the advantages of AI/ML extend beyond executing trades or gathering market signals. Information overload, coupled with time pressure, have sent asset managers scrambling to find a competitive advantage. AI/ML-enabled front-office solutions – provided by trusted service providers – are an innovative way of achieving a competitive edge quickly, without significant upfront investment or overhead.

Let’s look at where the AI/ML-driven “Front Office-as-a-Service” model makes perfect sense.

Credit monitoring

Traditionally, credit analysts have devoted massive amounts of time to physically examining documentation to understand the terms of the credit agreements. Now, service providers are deploying Natural Language Processing (NLP) applications to take over this labor-intensive task. A service that rapidly scans hundreds or thousands of pages, parses the most relevant information, and populates it into front-office investment models isn’t just smart. It also represents considerable value for front office managers by significantly reducing the time needed to complete this laborious but business-critical process.

Cutting-edge technologies extract relevant information contextually from an increasingly broad range of sources, including financial statements, conference call transcripts, press releases, industry databases, and social media. In such service models, information from these structured and unstructured data sources automatically populates financial models and risk monitors and forecast future scenarios.

Such Automation-as-a-Service gives asset managers an avenue to test the adoption and implementation of advanced technology in their in-house processes and realize the benefits of scale almost immediately. Furthermore, small to mid-sized asset managers can efficiently plan their resource deployment and budgets while benefiting from the outsourcing model’s efficiencies.

Real estate cashflow modeling and monitoring

Now let’s consider the real estate use case. An asset manager with significant real estate investments might own thousands of properties, with each deal structured differently in terms of leverage, cash flow timing, and other critical business factors. Multiple external aspects impact the ongoing portfolio management of these investments: foreclosures, default rates, interest rate swings, and changes in consumer confidence, among others.

Of course, one could use traditional statistical models to manage this complexity. But the sheer volume of information to be processed while one builds the cash flow models, performs waterfall analysis, and examines the impact of different market stress scenarios on these structures is highly complex – and potentially prone to error.

Here, service providers again rely on AI/ML to automate complex tasks and augment the speed and accuracy of the analysis and monitoring.

Portfolio companies

When it comes to private equity, monitoring portfolio companies is a laborious task. From an operational perspective, financial data – and increasingly, ESG data – is being reported by portfolio companies into the manager’s back-end systems in a range of different formats. The challenge this presents is further compounded by periodic progress reports on strategic initiatives, operating KPIs, and competitive analysis – also in a wide range of formats.

Some PE managers also focus on capturing real-time operating KPIs – such as invoices, billings, and footfalls – rather than waiting for monthly reporting. This adds to the pressure on PE teams to manage the volume of data and information at any given point of time – much less derive actionable insights.

This is another area where AI/ML-driven outsourcing solutions can deliver efficiencies and scale. Automation tools enable in-house investment teams to focus on critical investment decision-making. Meanwhile, service providers manage such tasks as monthly reporting follow-ups and real-time data extraction and processing, as well as populating financial models and updating portfolio review dashboards.

Data management

While data management services are nothing new, service providers that also deploy AI/ML have further strengthened their ability to offer a fully featured solution. Data Management-as-a-Service (DMaaS) provides a significant competitive advantage for asset managers as they rely on the service provider to extract, assimilate, clean, and process structured and unstructured information.

As service providers increasingly adopt such approaches, accessing the right talent and systems will become a source of competitive edge. AI/ML tools can process more information than an army of analysts. It can be an essential lever to pull, not only at the pre-investment analysis stage but also concerning ongoing portfolio management and value creation. Working with an AI/ML outsourcing partner is an increasingly enticing option for increasing efficiency and scalability while managing overhead costs.

Should you outsource?

If you’re thinking about taking the leap and introducing more automation into your front office, AI/ML offers a foundational layer upon which to build greater operational efficiency. Two clear areas where efficiency gains can be realized through ‘AI/ML-as-a-Service’ are research, and risk management.

Leveraging AI/ML technology – either in a SaaS model or as a full outsourcing or co-sourcing solution – will enable your analysts to focus on core investment functions. Rather than devoting your time to gathering and cleansing data and applying the appropriate risk model(s), you can leave that heavy lifting to your service partner.

Solution providers can deliver daily/recurring and ad hoc reporting and embed expert analysts as members of your virtual team, leaving your experts more time to focus on monitoring your portfolios and making sound investment decisions.

About the author, Rama Krishna

Rama Krishna is the Executive Director, Center of Excellence India at Linedata.