AI Financial Planning: How Robotics Help in Finance Management
Technology is changing the world at such a pace that businesses often have a hard time catching up and adapting to these changes. Currently, Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest fintech trends — even simple algorithms can analyze data and solve specific problems more effectively than humans, while the application of AI is predicted to save banks $447 billions by 2023.
For many industries, harnessing the power of AI is crucial for success in a highly competitive market, and the sphere of finance is one of them. The outdated practices of traditional wealth management get quickly substituted by robotics that offers personalization, market analysis, forecasting and automation of routine operations at a fraction of a price compared to personal financial planners. We at Surf have extensive experience in both fields of Machine Learning and developing fintech software, and in the article we’ll answer the questions how AI is used in finance and how it helps companies provide cost-effective wealth management services, resulting in customers getting personalized financial advice anytime, anywhere.
Application of AI on 5 steps of Financial Planning
Both individual and corporate financial planning can be regarded as a cycle of several main stages: from the assessment of the current situation to acting on a devised plan and, again, assessment of the results and changing the plan accordingly. Let’s take a look how AI and ML can help finance managers on every step of the planning, taking up routine tasks and offering deeper, more comprehensive analysis.
Learn & analyze the current situation
Any financial planning starts from understanding the person’s financial circumstances (or company’s if we talk about corporate planning). The data is usually gathered using a questionnaire focused on the client’s income, expenses, current portfolio & assets, liabilities, insurance coverage, health and family.
AI can help analyze a client’s profile more effectively. By collecting not only data provided by answering typical questions but also from other sources, such as bank accounts (on spending habits) and social networks (on the person’s lifestyle and material pursuits), AI can create a more complete and comprehensive profile for further financial planning and investment advice. Using mathematical models, machine learning is not only capable of more accurate conclusions from many variables than manual rules and formulas, it also delivers better results in case of missed, corrupted or anomalous data.
When Surf participated in the Russian Artificial Intelligence Forum 2017 championship, we used AI for real-time purchase probability prediction for M.video retail chain. To estimate how likely a user is to make a purchase while they are still browsing the store’s website, we used a convolutional neural network that analyzes client’s behavior using a mathematical model. This way, the AI was not only able to discover dependencies and correlations that go unnoticed to the human eye, but also optimize and increase the purchase probability in real-time. As a result, Surf’s solution won 1st place in the “AI for retail” nomination, while our solutions for banks and insurance entered the top of the best.
The next step, after initial profiling, is to set clear goals for a financial plan. There can be one or several goals, for instance, the desired return rate on investments, a sum of money necessary for a big purchase, or a retirement plan. It is important to prioritize each of the goals and establish a timeframe for accomplishing them. By using the data gathered on the first step, AI can help draw a roadmap to the goals and predict how much time it would take to reach them if the user continues to maintain the current rate of earning and spending or changes their habits in some way.
Assessment of a personal risk tolerance helps define the portfolio’s composition — whether there should be riskier assets with higher return potential or more conservative and safer investments. But while the majority of investment advisors base their recommendations on a user’s willingness to risk, in our investment app concept we decided to focus also on the user’s financial goals. A sharp downturn in stock prices can scare off an inexperienced investor, even though they’ve received a ‘high risk’ profile, and lead to losing a customer. An approach based on the user’s goals can help build a more diversified, and, as the result, more stable portfolio.
Develop a plan
Having a complete picture of the client’s financial situation and goals, AI can provide personalized wealth management suggestions. Powered by Machine Learning, modern robo advisors act as a personal finance AI and have many benefits compared to human managers: always unbiased and real-time advice, infinite scalability and lower costs. The growing popularity of stock and crypto trading makes robo advisors the best choice for individuals who don’t have deep investment knowledge and large amounts of funds.
When picking investment instruments, the AI can analyze a vast multitude of factors, including security’s risks, bond credit rating, portfolio size, diversification requirements, taxes and many more. Artificial Neural Networks are already widely used to predict stock prices by analyzing highs, lows, opening and closing prices. Combining these predictions with Natural language processing technology (NLP) that enables machines to understand human language, allows AI to analyze sentiment towards companies in the media and make recommendations on stocks and bonds even more accurately.
As well as individual investors, AI can help businesses with digital financial planning by detecting assets at risk to certain market exposures or modeling how political or social events would affect the company’s assets. Insurance, another important part of financial planning, can also be enhanced by AI, which can analyze all available policies and select the most beneficial and personalized insurance plan.
Another aspect where robots tremendously help humans is routine operations. Guided by AI, robotic process automation (RPA) can execute trades, invest and pay bills automatically, as well as help with resource-intensive jobs of corporate Financial Planning and Analysis (F&PA). According to studies, using artificial intelligence in finance costs around one-third of hiring an offshore employee and one-fifth of hiring an onshore one.
Casual investors don’t have time to follow charts all day — robo advisors used in trading apps can execute trades automatically and at regular intervals, for example, to reinvest dividends or invest a part of a salary once a month. Another benefit of trading automation is that AI can group orders in the same security to cut down brokerage fees for individual customers. In budget apps, a user can delegate the process of paying monthly bills to AI to avoid missing payments’ deadlines. A prominent example of a budget app is Acorns that automatically deducts and invests round-ups from the user’s every purchase.
Monitor & analyze performance
When the financial plan is set in motion, the work doesn’t stop completely. In the ever-changing market situation of today, it is important to continuously monitor performance and make timely adjustments to the plan. And AI can help with that too.
Artificial intelligence can process high volumes of raw data in a matter of seconds, delivering digestible text and visual reports. Unlike humans, who unintentionally tend to focus on one aspect of a problem, AI remains aware of even the smallest details. This makes financial robots useful for protecting corporate interests and predicting risks with help of the Monte Carlo method (risk-probability simulation) and other algorithms. Using AI, financial managers have a complete picture of the current situation to make the right decisions.
However, the machines can make the decisions too. For example, a robo advisor in an investment app can predict the client’s portfolio performance and automatically rebalance it to help them stay on track to their financial goal. AI in financial planning takes into account all the nuances that can be easily forgotten in a hurry or market panic, for example, to keep operations to a minimum to avoid unnecessary fees or to avoid picking up a similar stock as per IRS wash-sale rule that prohibits re-purchase of the same or identical instrument within a month after selling it.
To sum things up
Artificial Intelligence and Machine Learning are powerful allies on every step of the financial planning process for both individuals and businesses. Compared to human financial managers, robots have benefits of infinite scalability: they get cheaper and smarter the more data they process and the more clients they have. AI can comprehensively analyze complex financial metrics and offer in-depth insights regarding risks, liability, liquidity and other aspects, detecting patterns that often go unnoticed to humans.
Today, when there is a stable growth in general public interest towards investing in stocks and crypto, AI financial planning software offers the competitive advantage necessary to make investment advice affordable to people and deliver it right to their smartphones. And there are plenty of other fintech spheres where AI drives things forward, including insurtech and personal budgeting. Having extensive experience in both AI and Machine Learning, as well as developing apps for banks and trading platforms, we at Surf will be glad to discuss how we can help you with your project. If you’re interested, drop us a line.