Fintech Applications to Investment Management

Fintech Applications to Investment Management

Fintech has had a huge impact on investment management. The ability to create computer programs that can learn on their own and improve over time creates new opportunities for investment professionals across the board.

Applications of Fintech in the Investment Industry

Within the investment industry, fintech manifests in several ways:

Analysis of Loads of Data

We now have crowd-sourced content services that analyze large datasets consisting of security prices, financial statements, economic indicators and qualitative bits of information that a portfolio manager can integrate into a the investment decision-making process.

Complex algorithms have been developed to scour social media and sensor networks in search of consumer sentiments and product performance data that can be integrated into a manager’s buy or sell decisions.

Automated Trading

Artificial intelligence has enabled the analysis of extremely large datasets that could only have been analyzed by humans just a couple of years back. For instance, we now have systems built to identify systematic investment strategies and automatically execute multiple trades over several financial markets worldwide. As a result, investment banking departments that used to accommodate hundreds of traders have now been reduced to just a few individuals, supported by computer engineers.

Analytical Tools

We have machine learning algorithms built to sift through enormous amounts of financial data — company filings, earning calls, profit warnings, etc. These algorithms are then able to unearth trends and identify buy or sell stocks.

Automated Advice

Robo-advisors —  internet-based intelligence models that provide investment advice with minimal human intervention – have revolutionized wealth management. These models cost much less than traditional adviser models and human resources.



With over $13.5 billion in assets as of 2018, Betterment is one of the oldest Robo-advisors. Once signed up, the investor completes a short survey about their investment needs and risk tolerance. The information is then used to develop an automatic investment plan. Built-in systems automatically adjust the investor’s portfolio to maintain an optimal level of risk.


Wealthfront builds investors free financial plans and automates the investment process at a fee. Sophisticated models combine investor data with the relevant external data to build a portfolio that reflects the investor’s risk appetite.

Financial Record-keeping

New technologies such as Distributed Ledger Technology use independent computers to record, share and sync transactions in their respective electronic ledgers. This has eliminated the need for a centralized databank which is the case in a traditional ledger. DLT enables recording of “value” interactions without a need for a central coordinating entity.

Risk Management

Recent events such as the 2007/2008 finance crisis and the Greek debt crisis of 2015 have served to emphasize the importance of risk management. The global investment industry has, therefore, introduced a raft of measures that involve analysis of enormous amounts of data, including:

  • liquidity information of a company and its competitors;
  • balance sheet positions;
  • risk-weighted assets, and
  • cash flow positions.

To manage risks, the importance of real-time analysis of such data cannot be underestimated. As a result, big data models have been built to aggregate, analyze, and interpret these data. That way, it’s possible to identify weakening positions and adverse trends in advance.

Behind the torrential growth of fintech lies extremely rapid growth in data and the development of tools and the technical expertise to extract the data.


Which of the following factors is most likely behind the increased adoption of automatic algorithmic trading?

A. Increased efficiency

B. Increased market destinations

C. Ability to execute large trades

The correct answer is B.

Over time, financial markets have disintegrated into smaller markets consisting of electronic exchanges, alternative trading systems, and dark pools. Digital algorithms have made it possible to automatically execute multiple trades over several global financial markets. This has been their biggest selling point.

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