AI-Driven Decision Making: How Artificial Intelligence is Reshaping Asset Management

by October 28, 2021 0 comments

In 2019, the global asset management industry ended on a high and entered a new era of structural change. According to a BCG report, the industry has grown by 11% in 2020 to end the year at $103 Trillion. The world’s largest asset management region, North America, showed remarkable growth of 19%, or $7 trillion in value, due to a combination of strong consumer spending, historically low unemployment, and quantitative easing.

However, as asset flows soared, the asset management industry faced structural challenges brought on by fee compression and cost pressures. As a result, the sector reported a marginal decrease in profitability.

With the advent of structural change, regulatory changes widened quickly. A shift of focus from performance to other aspects like compliance, experience and reporting played a key role. This exacerbated the problem manifold. Today, utility teams face several unprecedented challenges –from ageing technologies to pandemic recovery.

Most businesses have been forced to modify and adapt to ensure safe and reliable service. Also, to optimise the performance of assets, making the right investment decisions has become more critical than ever.

Harpulak Bahadur, CEO, Intellimation

Harpulak Bahadur, CEO,

In this scenario, artificial intelligence (AI) is offering effective solutions to optimise performance and giving users a significant competitive edge. For asset managers, AI/machine learning (ML) is a boon as its advantages extend beyond executing trades or gathering market signals.

Time is a factor too. Information overload, coupled with time pressure, is part of everyday life for asset managers. Analysts spend hours, sometimes days, researching hundreds of sources manually. The process is labour-intensive and there is a high chance of irregularities creeping in. AI/ML tools can process pre-investment analysis, portfolio management and value creation. Earlier, data was not easily tapped or accessed due to monumental manual work involved in collation, aggregation, synthesis, and collecting data inference. The ability to analyse a massive amount of data, including uniform, non-uniform and unstructured data, is what makes AI dynamic.

Insights from vast chunks of data generated daily enable analysts to gain time to drive critical decisions that will grow revenue and achieve operational efficiencies through proprietary solutions.

Some analysts use AI and natural language processing (NLP) to extract the most relevant information from an unstructured database. This helps to make an informed decision with the ability to execute decisions on a real-time basis with high accuracy and helps to quantify anomalies and ensure appropriate pricing actions.

AI/ML-enabled front-office solutions offer innovative and cost-effective alternatives to achieve a competitive edge. Being accessible to both technical and non-technical end-users can be a significant innovation provided by financial services.

With the rise of AI/ML tools, existing fintech firms are growing and new firms are making an entrance. Many fintech firms provide innovative solutions, such as no-code AI, for businesses that can quickly implement AI and ML in their data sourcing processes. AI/ML will prove to be game-changing and the most significant foundational element that will transform the industry.

Author: Harpulak Bahadur, CEO,

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