FINANCE
THE APPLICATION OF AI IN FINANCEIt is a well-known fact that finances are rather a sensitive subject with way too many things that require laser-sharp focus and extreme delicacy. Stock analysis and prediction is rather complicated thing that requires a lot of computational that often get tangled and confusing due to the human factor. AI can do it instantly without breaking a sweat.
ISSUES WHICH CAN BE SOLVED WITH AI
In the next decade, Artificial Intelligence will help financial services companies maximize resources, decrease risk, and generate more revenue, in the trading, investing, banking, lending, and fintech verticals.
Maximizing Resources
Artificial Intelligence helps companies in the financial industry save time and money through the use of algorithms to generate insights, improve customer service, and make predictions about company sales performance and churn.
Filtering information and analyzing sentiment
AI helps humans work more effectively by filtering key information from a wide variety of sources. For example, AlphaSense‘s sophisticated search functionality leverages natural language processing to find and track relevant information in search, learning from successes and mistakes with each search. Reuters News Tracer filters tweets through Machine Learning algorithms to pick up on breaking news before it’s reported elsewhere.
Likewise, financial services companies can use AI to detect brand sentiment from social media and text data, measure it, and transform it into actionable advice. Sentiment analysis assists with advanced classification of textual data (e.g., for compliance). These would be relatively novel applications of artificial intelligence, particularly in the arena of finance.
Better trading
AI can help manage and augment rules and trading decisions, helping process the data and creating the algorithms managing trading rules. Investment firms have implemented trading algorithms based on sentiment and insights from social media and other public data sources for years.
Hong Kong-based Aidya uses algorithms to conduct trades autonomously, and some companies, like Japan-based Nomura Securities, relies on AI robo-traders for high-frequency trading, to boost profits.
Risk Management
Chatbots help banks serve customers more efficiently, even though they aren’t advanced enough to handle support cases autonomously. Powered by natural language processing, bots can listen in on agents’ calls, provide accurate answers quickly, and suggest best practice answers to improve sales effectiveness. Neural networks help agents respond to common customer service queries by sorting and labeling metadata and generating three potential responses, each with a level of certainty attached.
As we mentioned earlier, we’re already seeing banks like Wells Fargo using chatbots to improve the customer experience and reduce time and cost. Many of these virtual assistants use predictive analytics and cognitive technologies to personalize customer support, accessing a user’s financial portfolio, banking history, and goals, to automate trades and give advice. Predictive analytics are able to leverage a company’s customer base for churn prediction, advanced revenue prediction, and sales forecasting.
Financial firms take advantage of AI to identify the clients most likely to leave a bank or advisor. Finn.ai‘s white label chatbot integrates into existing messaging platforms, as well as a bank’s web chat interface.
If a financial firm’s data is unstructured, or the company has many databases that store information about entities separately, it’s difficult to link and connect information. An army of human analysts used to be required for such projects, but now, it can be done via AI, with minor human supervision.
Benefits of AI in the Finance Sector
Within the financial services sphere, established institutions are struggling to compete as legacy systems have become outdated and inflexible, reducing efficiency. In order to maintain competitiveness, machine learning holds the key to the future. As the machine continues to learn and analyse data, financial institutions can capitalise on various areas to enhance productivity and ultimately increase profitability.
- Customized financial services
- Reduction of cost in finance through artificial intelligence
- Fraud detection
- Less human intervention in management
Customized financial services
Artificial intelligence has expanded the range of offerings under the finance segment based on the customer preferences for financial spending. Data accumulated by AI suggests that there should be various customization in finance based products and services because the spending pattern of customers differs in many ways. There are some customers who look for specific offerings from a bank and he/she should receive the optimum package based on the need and want.
Reduction of cost in finance through artificial intelligence
We can all agree on this because AI has definitely brought the costs down in finance by providing multiple services at an affordable price. Nowadays, the services offered by banks are comparatively low in price which is good for a customer. There are various preferences when it comes to availing a certain service. AI has made it extremely convenient for the public to make use of the financial services.
Fraud detection
Artificial intelligence can proactively detect whether fraud is going to take place in a financial system or not. AI makes it a point to keep all things secure and take steps towards safety before any chances of fraud can occur. Fraud detection through AI can help bankers follow the policies and regulations while providing a financial service to an individual. AI is expanding the financial products portfolio by continuously understanding the human psychology.
Less human intervention in management
There is no longer a need for specific personnel to answer questions about financial services being offered and how they can help the customer. Now, AI processes data to solve queries and suggest the best service or solution for an individual without human intervention. Human opinions are no longer needed to forecast the demand of financial services.
Interesting Projects & Applications of AI in the Finance Sector
Anti-money laundering AML Pattern Detection
Anti-money laundering (AML) refers to a set of procedures, laws or regulations designed to stop the practice of generating income through illegal actions. In most cases, money launderers hide their actions through a series of steps that make it look like money that came from illegal or unethical sources are earned legitimately.
Most of the major banks across the globe are shifting from rule-based software systems to artificial intelligence based systems which are more robust and intelligent to the anti-money laundering patterns. Over the coming years, these systems are only set to become more and more accurate and fast with the continuous innovations and improvements in the field of artificial intelligence.
Chat bots
Chatbots are artificial intelligence based automated chat systems which simulate human chats without any human interventions. They work by identifying the context and emotions in the text chat by the human end user and respond to them with the most appropriate reply. With time, these chatbots collect a massive amount of data for the behaviour and habits of the user and learn the behaviour of the user which helps to adapts to the needs and moods of the end user.
Chatbots are already being extensively used in the banking industry to revolutionize the customer relationship management at personal level. Bank of America plans to provide customers with a virtual assistant named “Erica” who will use artificial intelligence to make suggestions over mobile phones for improving their financial affairs. Allo, released by Google is another generic realization of chat bots.
Algorithmic trading
Plenty of Hedge funds across the globe are using high end systems to deploy artificial intelligence models which learn by taking input from several sources of variation in financial markets and sentiments about the entity to make investment decisions on the fly. Reports claim that more than 70% of the trading today is actually carried out by automated artificial intelligence systems. Most of these hedge funds follow different strategies for making high frequency trades (HFTs) as soon as they identify a trading opportunity based on the inputs.
A few hedge funds active in AI space are: Two Sigma, PDT Partners, DE Shaw, Winton Capital Management, Ketchum Trading, LLC, Citadel, Voleon, Vatic Labs, Cubist, Point72, Man AHL.
Fraud detection
Fraud detection is one of the fields which has received a massive boost in providing accurate and superior results with the intervention of artificial intelligence. It’s one of the key areas in the banking sector where artificial intelligence systems have excelled the most. Starting from the early example of successful implementation of data analysis techniques in the banking industry is the FICO Falcon fraud assessment system, which is based on a neural network shell to deployment of sophisticated deep learning based artificial intelligence systems today, fraud detection has come a long way and is expected to further grow in coming years.
LEADING PROVIDERS OF AI SOLUTIONS FOR THE FINANCIAL SECTOR
R3 CORDA
Fenergo
Digital Asset
Digital Asset combines unparalleled financial markets leadership with world-class technologists across multiple fields in one of the fastest growing fintech companies in the world. The Digital Asset Platform is the only Distributed Ledger platform to have been developed according to the production requirements of the world’s largest financial institutions.
