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Asset Management Is Being Completely Disrupted By Data Science. Here’s How.

Asset Management Is Being Completely Disrupted By Data Science. Here’s How.


The financial services industry has always been working with large volumes of data and when it comes to asset management, the data volume increases multi-fold. The last decade has witnessed massive growth in the financial services industry in terms of data analytics technologies. While the early algorithms used structured data only, modern machine learning based solutions can yield insights even from highly unstructured records.



Moreover, sentiment analysis and image recognition have now been employed to assume potential peaks and valleys in the stock market. For example, collecting and analysing social media trends around brands helps the trader foresee whether a company’s stock prices will rise or fall.

Despite the changing trend, traditional wealth management companies continue to remain late adopters of the technologies and are still seeking ways to become data-driven. Here are the main operations that can be enhanced with a data-driven approach.

Data-driven asset management:

1. Smart advisors (or robo-advisors): These advisors have been around for almost a decade and have now become the hottest personalisation trend in the financial management industry. The algorithms consider various customer data – risk tolerance, behaviour, legal benchmarks, preferences – and make recommendations based on this data.

By combining multiple data sources, one can increase the dimensionality of models and solve complex optimisation problems that account for hundreds of individual portfolio factors. This allows portfolio managers to suggest tailored investment plans to clients in both B2B and B2C operations.

2. Fraud detection powered by neural networks: Another emerging trend in financial management are anti-money laundering and fraud-detection models that are powered by neural networks and help in identifying any suspicious activities.

The system is trained and developed in a way that it can track and assess the behaviour of all the individuals involved in the process. The systems use and apply deep neural networks to detect any fraud by analysing both structured and unstructured data that include all kinds of online footprints. The strong neural networks efficiently detect any implicit link between the customer and any potential fraud.

3. Predictive analytics: Predictive analytics uses historical data to determine the relationships of data with outputs and build models to check against current data. Stocks, bonds, futures, options, and rates movements form the stream of billions of deal records every day, which make for non-stationary time series data. These often become complex problems for financial analysts because conventional statistical methods fall short both in terms of prediction accuracy and speed. There are three approaches to combat these data.

Machine learning methods: Models are trained on short-term historical data and yield predictions based on it.

Stream learning: A predictive model is continuously updated by every new inbound record, which provides better accuracy.

Ensemble models: Multiple machine learning models analyse incoming data, and the predictions are based on consolidated forecasting results.

4. Scenario-based analytics: The method lets financial managers to analyse possible future events by considering alternative possible outcomes. Instead of showing just one exact picture, it presents several alternative future developments. Computing power and new data processing packages have made building stress models for company operations and stock market performance possible.  With this method, one can test millions of scenarios accounting for hundreds of unique market conditions.

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Why must asset managers start adopting technology?

There has been much talk about money managers being slow in adopting technology for asset management. Upgrading to digitisation will weaken the risk of these players to lose market share to the digitally savvy businesses that are aiming to disrupt the investment industry.

According to a poll conducted by Create Research, out of the 458 asset and wealth managers, only 27 per cent of wealth managers offer robo-advisers, and only 31 per cent use big data. The asset management industry’s need to modernise comes as it is grappling with pressures ranging from tougher regulation to stronger competition.

The other reason that should also be considered for making the shift are the millennials. They are not just digitally savvy but are also potentially rich. Just to give a sense, the millennials will soon make for the largest part of the workforce and also stand a strong chance to inherit ancestral wealth, which could approximately be $15tn in the U.S. and $12tn in Europe over the next 15-20 years, Create Research said. With all that money and digital savviness, financial advisors should equip themselves to stand a chance in the growing competition.

Conclusion:

Adopting data science solutions for wealth management is not new in the financial market. However, wealth management organisations have continued to be the late adopters of data-driven technologies. Yet, there is no denial to the fact that industry leaders have been the first ones to adopt these technologies and have set a benchmark for the others to meet.

The data science technologies for wealth management is the next big thing into the field. These technologies have the capability to intensify the interest in semantic analysis, ML-based time series forecasting, and even scenario-based modelling.

Due to a fairly late transformation as compared to the financial services industry in general, the smart move today will be to seek a partnership among tech consultancies and fin-tech start-ups to avoid reinventing the wheel.


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