8 Finance AI and Machine Learning Use Cases for 2022

Using robo-advisory is more cost-effective than using a traditional advisor, provides opportunities that traditional analysis may otherwise overlook, and eliminates time-consuming tasks such as rebalancing and checking proper asset allocation. Train your AI model on V7 to automate the document scanningOne of the techniques that comes in handy for automation is the already mentioned optical character recognition. Process automation is an interesting option for businesses looking to hire or outsource their financial processes, as well as for professionals who wish to streamline internal processes. OCR can automatically recognize and extract data from scanned documents and images in a structured way and helps in reducing processing times for each document. Leading lenders, like Ally, are also automating the process of approving the loan and predicting the maximum amount a customer may borrow and the pricing of the loan using AI and ML models. The bank previously employed a team of lawyers and loan officers who used to spend 360,000 hours each year tackling mundane tasks and reviewing compliance agreements.

While these processes still require improvement, AI-powered security solutions will eventually replace traditional usernames and passwords. Now that we have looked into the real-world examples of artificial intelligence in banking, let’s dive into the challenges that exist for banks using this emerging technology. Governments use their regulatory authority to ensure that banking customers are not using banks to perpetrate financial crimes and that banks have acceptable risk profiles to avoid large-scale defaults. Currency fluctuations, natural disasters, or political unrest have serious impacts on banking and financial industries. During such volatile times, it’s crucial to take business decisions extra cautiously.

Benefits of Using AI for Financial Services

The assistant provides services ranging from simple knowledge and support requests to personal financial management and conversational banking. The company partnered with financial news giant Bloomberg to provide users with its “AlpacaForecast AI Prediction Market.” The program predicts short-term forecasts in real-time for major markets. Alpaca combines proprietary deep learning technology and high-speed data storage to provide short and long-term forecasting applications. Alpaca’s technology also identifies patterns in market price-changes and translates its findings into multi-market dashboards. The following companies are just a few examples of how AI is helping financial and banking institutions improve predictions and manage risk. Since artificial intelligence is used to analyze patterns within large data sets, it’s no surprise that it’s often used in trading.

AI could serve the entire chain of action around a trade, from picking up signal, to devising strategies, and automatically executing them without any human intervention, with implications for financial markets. Thanks to document capture technologies, financial institutions can automate their credit applicant evaluation processes. Instead of reviewing financial documents like payslips or invoices manually, which is a tiring task, AI algorithms can handle this operation, capture data from documents automatically, and manage lending operations with less human intervention. This will enable banks and financial institutions to conclude credit applications faster and with fewer errors. Nexocode developed an AI system based on credit history and other data points for loan default prediction to enable automated loan eligibility assessment and to identify risks of client defaulting with loan payments.

Personalized banking experience

Customers can trade stocks and shares through mobile apps with AI-powered decision-making thanks to AI in fintech. Complex sentiment analysis, which focuses on finding deficiencies, training chatbots, and enhancing customer experience, aids AI in boosting financial customer care. Financial fraud has increased in recent years, ranging from loan applications to credit card schemes to fraudulent wire transfers and fake insurance claims.

Machine Learning powered solutions allow finance companies to completely replace manual work by automating repetitive tasks through intelligent process automation for enhanced business productivity. Chatbots, paperwork automation, and employee training gamification are some of the examples of process automation in finance using machine learning. This enables finance companies to improve their customer experience, reduce costs, and scale up their services. AI tools and big data are augmenting the capabilities of traders to perform sentiment analysis so as to identify themes, trends, patterns in data and trading signals based on which they devise trading strategies. While non-financial information has long been used by traders to understand and predict stock price impact, the use of AI techniques such as NLP brings such analysis to a different level. Text mining and analysis of non-financial big data with AI allows for automated data analysis at a scale that exceeds human capabilities.

Fraud Detection and Risk Management

Also, the use of natural language processing allows machines to read and analyze support tickets in a human-like way. Such ML techniques as sentiment analysis, ticket categorization, and keyword analyzer can help tailor a custom solution to specific support service needs. AI-powered solutions are scalable and can help achieve up to 100% automation depending on the nature of your business processes.

It can be expected in the near future to see companies relying on AI to make significant firm related decisions. AI also has the capability to identify how customers are going to react to various situations and problems. Artificial Intelligence is going to help people and firms make smarter decisions at a very quick pace. Working like regular advisors, they specifically target investors with limited resources (individuals and small to medium-sized businesses) who wish to manage their funds. These ML-based Robo-advisors can apply traditional data processing techniques to create financial portfolios and solutions such as trading, investments, retirement plans, etc. for their users. Banks are generally equipped with monitoring systems that are trained on historical payments data.

Implementing AI Tech Solutions

Now smart contracts can be changed and voted on by the community/DAO, and smart contracts emerged as one of the most efficient and effective data management solutions. Adding AI on top of this can help overcome data management challenges including automation. Investment companies have started to use AI to detect the patterns in the market and predict their future values.

AI and blockchain may not completely overhaul the financial industry as we know it, but they will most definitely change how we interact with financial data. By leveraging AI technologies like natural language processing and data extraction models, banks can find anomalous patterns and identifying areas of risk in their KYC processes without human intervention. For edge cases where human interaction is needed, the case can be forwarded for approval. The integration of AI technologies will have benefits like accelerated processing times, improved security and compliance, and reduced errors in these processes.

Top machine learning tasks

These smart systems can track regular expenses, income, and purchasing habits to provide the necessary financial suggestions and optimized plans. Cybercriminals constantly develop new, more effective tactics, but AI-based solutions can use machine learning and quickly adapt to the hackers’ strategies. It makes sense to expect machine learning to be used not only for various automation and customization tasks but also to news trends, social media, and other sources of data that have nothing to do with trades and stock prices.

AI in finance is still in its developmental stages, and it continues to proliferate with the help of data scientists. Despite the disruptive innovations that have stemmed from it, it’s undeniable that more incremental and architectural innovations will crop up in financial AI in the coming years. The AI would notice anomalies in purchase behavior and block the card before further damage ensues. AI can also predict the probability of the consumer defaulting and prevent the credit from being extended, thus saving the bank from a bad loan. AI mitigates such risks using advanced analytics and predictive analytics to spot specific patterns and reduce risk when these patterns are violated. ‌Over the years, AI has found its way into every single industry, disrupting the norm and status quo.

• For instance, a large European bank has successfully implemented AML and KYC analysis for client onboarding processes.
• Moving ERP to the cloud allows businesses to simplify their technology requirements, have constant access to innovation, and see a faster return on their investment.
• Although there are some risks involving ethics, data protection, regulations, security, governance, and transparency, financial institutions have to minimize such risks and mitigate the impact if they occur.
• Though it cannot be defined as general intelligence, rather it is designed to act intelligently towards completing the narrow tasks that are assigned to it.
• The finance industry, including the banks, trading, and fintech firms, are rapidly deploying machine algorithms to automate time-consuming, mundane processes, and offering a far more streamlined and personalized customer experience.
• The potential lender will receive a credit score that serves as a basis for the decision-making process.

Finance providers need to have the skills necessary to audit and perform due diligence over the services provided by third parties. Over-reliance on outsourcing may also give rise to increased risk of disruption of service with potential systemic impact in the markets. Similar to other types of models, contingency and security plans need to be in place, as needed , to allow business to function as usual if any vulnerability materialises. As such, rather than provide speed of execution to front-run trades, AI at this stage is being used to extract signal from noise in data and convert this information into trade decisions. As AI techniques develop, however, it is expected that these algos will allow for the amplification of ‘traditional’ algorithm capabilities particularly at the execution phase.

Some companies base their business on that concept and provide git-like version control for data. Another crucial area in which machine learning can make a tremendous impact is fraud prevention. By fraud, we understand any fraudulent activity, such as credit card fraud, money laundering, etc. The former has been growing exponentially How Is AI Used In Finance in recent years due to the increased popularity of e-commerce, the number of online transactions, and third-party integrations. With AI poised to handle most manual accounting tasks, the development and proficiency of higher-level skills will be imperative to success for the next generation of finance leaders.

• While certain parts of these systems can focus on trying to predict asset returns , other components might use a more traditional approach based on econometrics and asset allocation theory.
• One bank taking advantage of AI in consumer finance is JPMorgan Chase.For Chase,consumer bankingrepresents over 50% of its net income; as such, the bank has adopted key fraud detecting applications for its account holders.
• Furthermore, AI chatbots keep on learning about the usage pattern of a particular customer.
• Between growing consumer demand for digital offerings, and the threat of tech-savvy startups, FIs are rapidly adopting digital services—by 2021, global banks’ IT budgets will surge to $297 billion.
• That is not the case for AI-based solutions, which can evolve over time and adapt to new patterns found in the data.
• Alternatively, data scientists can participate in Numerai — a Kaggle-like data science challenge where the goal is to predict stock market returns based on the provided data and potentially external, alternative data.