The concept of artificial intelligence (AI), described as the development of computer systems capable of performing tasks which, in general, require human intelligence, arose more than 60 years ago. However, this technology did not reach the field of financial services until the early 1980s. It had limited implementation and use at that time due to the state of the technology and power of computer systems. But in recent years there has been significant growth in injecting AI into the financial planning process.
Origins of artificial intelligence in the financial sector
In the early 1980s, for example, the Citibank Investment Bank attempted to build expert systems, using artificial intelligence that imitated the decision-making power of a human expert. And Citibank was not the only one, many other Wall Street companies launched similar projects at that time.
And in 1987, the Security Pacific National Bank launched a Fraud Prevention Task Force to automatically counter, through the use of artificial intelligence, unauthorized use of debit cards at ATMs and stores.
These projects were successful to some extent, resulting in some useful applications, especially with regards to the use of artificial neural network systems to automatically detect unusual charges or claims that could subsequently be investigated by a human.
Despite these advances, companies soon realized that the development of artificial intelligence systems was costly and required more time than originally anticipated; and decided to abandon further development.
In the 90s, financial institution utilization of AI was fairly limited. However, universities and research groups continued to conduct research to advance AI. We are now once again seeing a significant push to integrate AI into financial institutions.
What has changed in the last 20 years for the result to be different now?
A series of scientific and technological advances that facilitate the development of artificial intelligence today has enabled a recent widespread revival of this field:
- The advances in hardware and software have been amazing with pure processing power having significantly increased year after year.
- The development cost of high-powered computing equipment has been considerably reduced, so now companies have much more powerful computer platforms for much less money.
- The widespread use of social networks, smart mobile phones, tablets and wearables, along with advances in sensors and their installation in smart cities and the rise of the “ Internet of Things”, has generated large volumes of data (e.g. big data), ideal for powering artificial intelligence engines and thus enable them to have enough input to analyze and make recommendations.
In recent years, artificial intelligence experts have moved from universities to the business world and are now beginning to see results in the business sector. Most large technology companies, such as Google, Facebook or Microsoft already use AI in their best-known products. And over the last few years, the press has been filled with headlines related to artificial intelligence.
Given the clear emergence of new technologies in the financial sector and the rapid rise of the Fintech (financial technology) more applications are being brought to market.
One of the main applications of artificial intelligence in all sectors is the personalization of the user experience, by allowing the different tools to be adapted, through an automatic learning process, to the preferences of different users. And, once again, the financial sector is no exception.
Although there is still much to come there are already personal banking applications that use artificial intelligence to interact with users and adapt to their preferences and needs. Banks also use AI systems to organize their operations, invest in securities and manage the property, and as a means to monitor for illegal trading. There are also advisory robots that can make investments, and depending on the information they have at their disposal about the preferences of the investor. They can automatically decide what the best investments for an investor and make trades.
In fact, back in 2001, in a human versus AItest, the AI outperformed humans in a simulated financial trading competition. That promise has led to more AI driven trading. According to CNBC over 700 million dollars have been invested in AI in 2015 and 2016. And the changes that have taken place have revealed the enormous potential of this technology to increase revenues, reduce costs and minimize risks. For the financial sector, AI could create operational efficiencies in financial planning and other areas ranging from trade to insurance underwriting and claims.
Robo-Advisory financial planning- What is a Robo-Advisor and How Does It Work?
Robo-advisors are digital technology platforms providing automated financial planning services based on algorithms with minimal supervision by humans. Today, customers have a direct access to robo-advisor services with many investment houses. Unlike their human counterparts, robot-advisors monitor markets without stopping and are available 24/7. Robo-advisors can offer investors up to 70% savings in costs and usually do not require a minimum to participate.
Robo-advisory is just starting to impact personal finance and wealth management sectors. While the total assets under management (AUM) of current robo-advisors represent $1trillion of the $74 trillion wealth management industry (less than 1% of all managed assets account), Business Insider estimates that this figure will increase to 10% by 2020. This amounts to about $8 trillion in assets under management (AUM).
Artificial intelligence trade – the transition from man-made models to true AI
For years, investment management firms have relied on computers to handle the trade. About one 1,360 hedge funds, which account for 9% of all funds, depend on large statistical models built by data scientists who often hold doctorates in mathematics. However, these models only use historical data, are often static, require human intervention, and do not react immediately when the market changes. As a result, the funds are migrating to increasingly more real-time AI models, which can analyze huge volumes of data, as well as continually improving their analysis models based on results.
Headed Toward the Future
Governor of the Bank of Japan, Haruhiko Kuroda, mentioned at a conference on AI and Financial Services in 2017 that:
“It is important that we consider constructively the desirable ways in which human beings and AI complement each other rather than confront one another. For example, human judgment is not totally free from existing paradigms and is sometimes lax to change, AI could adjust our bias through neutral analysis and find new correlations between a myriad of data, while humans could complement the weakness of AI with their intuition, imagination, and common sense.” (Quote Reference)
With the help of machine learning in robo financial advisory, there will be even spreading of the benefits of financial advice across consumers regardless of the size of the investment. A human adviser may be biased in favor of those with more cash or a client who demands extra attention, but this is not the case for robo-advisors.
Apart from reducing the human biases of investing, using an AI-based financial planning software can also reduce the time and cost involved in investing because management fees will decrease when robo-advisors assist or replace human advisors.