Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google’s Tensor flow).
Machine learning has become more accessible in many industries within the last few years, and that includes in the often closed-doors world of finance. Though machine algorithms have been used in finance for more than a decade, use of such methods was typically limited to those using quant funds, teams trying to get as much data as possible (before big data was a buzz word) and analyze the information to determine best trading strategies.
1) Client Credit Risk Management of Commercial Banks
A critical part of banks and insurance companies’ job is the profiling of clients based on their risk score. AI is an excellent tool for this as it can automate the categorization of clients depending on their risk profile, from low to high. Building on the categorization work, advisors can decide to associate financial products for each risk profile and offer them to clients in an automated way (product recommendations).
For this use case, classification models such as XGBoost or Artificial Neural Network (ANN) are trained on historical client data and pre-labeling data provided by the advisors, which eliminates data-induced bias. In this scenario, a commercial bank has incomplete historical data due to lagged credit risk management. An ANN-based credit risk identification model can perform online learning as data is accumulated over time— a task unachievable by traditional credit risk measurement models.
The credit risk identification model is constructed based on an ANN Back Propagation (BP) algorithm. The ANN-based model is first trained on the algorithm according to historical data. Then, the model can be used to identify the credit risk of the debtor firms, providing decision supports to credit risk control. Case study of how J.P. Morgan is committed to understanding how this technology-driven landscape could differentiate your stock, sector, portfolio, and asset class strategies.
2) Algorithmic Trading integration with Deep learning
Algorithmic systems often making thousands or millions of trades in a day, hence the term “high-frequency trading” (HFT), which is considered to be a subset of algorithmic trading. Most hedge funds and financial institutions do not openly disclose their AI approaches to trading (for good reason), but it is believed that machine learning and deep learning are playing an increasingly important role in calibrating trading decisions in real time.
An example of this is trading individual stocks versus price movements in the S&P 500 index, which is a known leading indicator (i.e. stocks follow the index). The algorithm takes the price movement from the index and predicts a corresponding move in the individual stock (ex: Apple). The stock is then bought (or sold) immediately with a limit order placed at the prediction level, in hopes the stock reaches that price.
The Artificial Intelligence (AI) application for trading on digital currency transactions is a popular method that has instantly increased throughout the system. The Advanced Artificial Intelligence is expanding in all the industries. In AI algorithms, neural structures are the foundation for system and services are prepared within that in an easy method. In the machine learning, artificial neural systems create a domestic of learning models, formed akin biotic neural complexes. The Artificial Intelligence impacting Algo Trading in multiple strategies with a combination of machine learning, deep learning, neural networks and linear deteriorations for optimizing algorithms in the system. Case study alpha-quant & Goldman Sachs is one of the the leader working on Hedge Funds and Quant trading models.
3) Augmented research tools with NLP & AI
In investment finance, a large portion of time is spent doing research. New machine learning models increase the available data around given trade ideas. Credit analysts subscribe to scores of premium news & research websites which they review daily for ideas to direct their research. They also scan the public web for relevant information on the companies they are writing about.
Sentiment analysis can be used for due diligence about companies and managers. It allows an analyst to view at a glance the tone/mood of large sets of text data such as news or financial reviews. It can also provide insight into how a manager reflects their company performance.
Satellite Image Recognition can give a researcher insight into many real-time data points. Examples of such are parking lot traffic in specific locations (retailer shops, for example) or freighter traffic in the ocean. From this data, the model and the analyst can derive business insights such as the frequency of shopping at specific stores of the retailers mentioned above, the flow of shipments, routes, and so on.
Advanced NLP techniques can help a researcher analyze a company financial reports quickly. Pulling out key topics that are of most interest to the firm. It helps save equity analysts at least 2-3 hours per day since their content is now at their fingertips. No more manual logins and web search. Furthermore, the news can be easily filtered so the analysts can quickly find what they are looking for. Case study Goldman Sachs & one fintech top 30 Startup accern is working brilliantly in this field.
4) AI and Fraud Prevention
For a number of years now, artificial intelligence has been very successful in battling financial fraud — and the future is looking brighter every year, as machine learning is catching up with the criminals.
AI is especially effective at preventing credit card fraud, which has been growing exponentially in recent years due to the increase of e-commerce and online transactions. Fraud detection systems analyze clients’ behavior, location, and buying habits and trigger a security mechanism when something seems out of order and contradicts the established spending pattern.
Banks also employ artificial intelligence to reveal and prevent another infamous type of financial crime: money laundering. Machines recognize suspicious activity and help to cut the costs of investigating the alleged money-laundering schemes. One Case study reported a 20% reduction in the investigative workload.
Aggregators like Plaid (which works with financial giants like CITI, Goldman Sachs and American Express) take pride in their fraud-detection capabilities. Its complex algorithms can analyze interactions under different conditions and variables and build multiple unique patterns that are updated in real time.
Combine more accessible computing power, internet becoming more commonly used, and an increasing amount of valuable company data being stored online, and you have a “perfect storm” for data security risk. While previous financial fraud detection systems depended heavily on complex and robust sets of rules, modern fraud detection goes beyond following a checklist of risk factors – it actively learns and calibrates to new potential (or real) security threats.
This is the place of machine learning in finance for fraud – but the same principles hold true for other data security problems. Using machine learning, systems can detect unique activities or behaviors (“anomalies”) and flag them for security teams. The challenge for these systems is to avoid false-positives – situations where “risks” are flagged that were never risks in the first place. Case Study from Forbes saying how AI implementation in Fraud detection helping financial institutions.
5) AI and Fundamental investing: The artificial intelligence analytical revolution
Over the past 20 years, investment management has undergone an aggregation revolution, where advances in information technology have sped up the depth, width and speed of information reaching investors. In the next five years, investment management will go through an analytical revolution, AI and investing will come together and revolutionize the way that investment information is analyzed, packaged and presented to investors.
For the first time, artificial intelligence can now bring a whole new perspective to investment decision making. The power of AI is its ability to tirelessly look for, combine, and distill signals from masses of noisy data already available in the marketplace. By bringing out “interesting” insights, whether to confirm or enhance a suspected salient point or by identifying one that might have been overlooked otherwise, AI is the humble ‘idiot-savant’ that can usefully take on the tedious data-intensive work that humans are not best suited for. For example, by deploying artificial intelligence to analyze investment information, the investor can
a) Rely on smart monitoring systems as markets, portfolios and holdings’ alerts get derived from an array of signals, not simply flag an absolute share price declines. The AI engine may alert the investor, often before most of the price decline, to consider selling all or part of a holding, or reducing exposure to a risk factor, or indeed looking into the recent news flow of a company for possible reasons to turn more bearish (or bullish) on the stock. Such a development vastly contributes to reducing investor biases with less vigilance required (less decision fatigue), less false alarms (less over-trading), and more balanced perspectives (less overconfidence), all contributing to better investor performance.
b) Benefit from the limitless scalability and versatility of the technology. Modern user interface and data analytics facilitate the delivery of complex combinations of data sources, and cloud-based technology makes it cheap to do so. All of the above can also be applied to inform investors’ fund selection and performance monitoring, so rather than relying on top-down backward-looking analysis, AI can deliver instant insights based on a bottom-up analysis of the funds’ holdings. J.P.Morgan Chase study will give us a complete picture how they are working
AI in Banking – An Analysis Report of America’s Top Banking Industry
JPMorgan: The firm invested over $9.5 billion in technology in 2016, with $3 billion “dedicated toward new initiatives” and a $600 million fraction slated for “emerging fintech solutions.” Specific interests include partnerships with fintech companies and developing new and enhancing current digital and mobile services. Different use cases of AI which brought a change in Fintech Industry by J.P Morgan
Wells Fargo & Company: So they announced that mobile banking deposit customers nationwide now have access to a predictive banking feature that analyzes account information, providing mobile app users with tailored account insights and personalized financial guidance. The feature is the latest in a series of innovations aimed at providing customers a more comprehensive view of their finances, and increased control to advance their financial health and meet financial goals. Wells Fargo is simplifying and enhancing its customers’ digital experience through a series of recent product announcements including Control Tower, Zelle person-to-person payments, data exchange agreements with several popular financial management tools, an artificial intelligence-driven chatbot experience pilot for Facebook messenger, digital wallets, card-free ATMs, and more. Financial report of 2018.
Bank of America: The company’s reported $3 billion innovation budget in 2016 and the Annual Technology Innovation Summit held in Silicon Valley “to explore potential partnerships, discuss trends and solve some of today’s challenges” positions the company firmly to maintain a bird’s eye view on the latest innovations in fintech. 2016 marked the second-most profitable year in the company’s history and with continued and strategic investment in technology and AI the company is poised for continued record-breaking growth. There use cases in AI.
Artificial Intelligence (AI) is a fast-evolving technology, gaining popularity all around the world. Several industries have already adopted AI for various applications, getting better and smarter day by day. In the past few years, the banking sector has also become one of the leading adopters of Artificial Intelligence. Most banks and financial institutions are implementing AI to add more efficiency to their back-office and lessen security risks.
As per Statista, the AI market in the United States is forecasted to reach 7.35 billion U.S. dollars in 2018. Some major applications of AI include classification, image recognition, object identification, and automated geophysical feature detection. Speaking of banking and financial institutions, JPMorgan Chase, Wells Fargo, Bank of America, Citibank, and other leading U.S. banks have already implemented AI in their systems, helping consumers manage their daily banking needs more efficiently.