25 Mar 2022, Siddhant Ojha

AI In Finance

"MACHINE INTELLIGENCE IS THE LAST INVENTION THAT HUMANITY WILL EVER NEED TO MAKE."-NICK BOSTROM

“Predicting the future isn’t magic , it’s artificial intelligence.”

Investing in stocks, cryptos, NFTs and other financial instruments of modern times seems very interesting but finding the right investments all the time is a very hectic task…

While one can go through annual reports, quarterly results, or telegram feeds (shhhhh SEBI might catch ya)… going through hundreds of pages of data for an already occupied person is not the thing of modern times.

We at OnFinance have just the right thing for you. We use complex AI & NLP algorithms that read through the noise of the investment world and bring out just the right investment ideas for you.

Hold on… hold on… hold on… didn’t quite catch what just happened? That’s exactly where we come in… we use Artificial Intelligence and Natural Language Processing to filter out stocks based on news and give you a Projected Market Impact of the news on the stock price… all while allowing you to go to the stock’s page on your broking app allowing you to place a buy or sell order in just one go.

BUT WHY A.I.?

Finance is all about data, be it analyzing stocks for trading or investing and using the most powerful and automated data analyzing technology is just the right way to go. The use of AI and NLP have substantially increased across a broad spectrum of industries within the world of finance. For example in retail and corporate banking (tailored products, chat-bots for client service, credit scoring and credit underwriting decision-making, credit loss forecasting, anti-money laundering (AML), fraud monitoring and detection, customer service, natural language processing (NLP) for sentiment analysis); asset management (robo-advice, management of portfolio strategies, risk management); trading (AI-driven algorithmic trading, automated execution, process optimisation, back-office) and much more. The deployment of AI techniques in finance helps bring down friction costs (e.g. commissions and fees related to transaction execution) and improves efficiency by increasing productivity, which leads to higher profitability. The use of AI is also very significant when it comes to personal finances and individual investing especially for regular retail investors and proper utilization of these tools add significant value to the wealth creation process of regular investors.

This is where we at OnFinance hop in and use our state of the art A.I. and NLP technology which searches through 200+ news sources (especially optimized for Indians) and gives Real Time Analytics, Projected Market Impact of the news and Riskiness of the financial entity be it Stocks, Cryptocurrencies or Non-Fungible Tokens (NFTs).

USE CASE OF A.I. in Finance

Data has become a crucial component for helping companies grow and reinvent their businesses. Organizations now find themselves in a new position where they must use data and AI to responsibly fuel their innovation, business models, and partnerships. Artificial Intelligence has turned out to be the most revolutionary technology when it comes to optimization of the process of analyzing large amounts of Data at a rapid rate.

This brings us to one of the use cases of NLP which is creating Financial Data Analyzer. NLP empowers finance professionals to read and comprehend large volumes of financial documents automatically.Businesses can train NLP models utilizing their existing documentation resources. Then, the NLP-backed financial statement analyzer swims through hundreds of these documents to extract and consolidate the most relevant, insightful information. Furthermore, NLP is instrumental in creating a search engine for financial market developments. There are tons of documents stored in the databases of financial institutions. The NLP-empowered search engine retrieves the elements, concepts, and notions present in these documents to obtain valuable investment data.The system then displays a summary of the most relevant information for search requests from financial firm employees on the search engine interface.

Another use of NLP comes in the form of Risk Assessment.

Banks can quantify the chances of a successful loan payment based on a credit risk assessment. Usually, the payment capacity is calculated based on previous spending patterns and past loan payment history data. But this information is not available in several cases, especially in the case of poorer people. According to an estimate, almost a half of the world population does not use financial services due to poverty.

NLP is there to solve this problem. NLP techniques use multiple data points to assess credit risk. For instance, NLP can measure attitude and an entrepreneurial mindset in business loans. Similarly, it can also point out incoherent data and take it up for more scrutiny. Even more, the subtle aspects like lenders’ and borrowers’ emotions during a loan process can be incorporated with the help of NLP. Usually, companies capture a lot of information from personal loan documents and feed it into credit risk models for further analysis. Although the collected information helps assess credit risk, mistakes in data extraction can lead to the wrong assessments. Named entity recognition (NER), an NLP technique, is useful in such situations. NER helps to derive the relevant entities extracted from the loan agreement, including the date, location, and details of parties involved.

The case of the Indian Banking System-

Highlights: AI in Indian Banking

  • In India, 32% of financial service providers are already AI in Indian Banking using AI technologies like predictive analytics, voice recognition among others, according to joint research conducted by the National Business Research Institute and Narrative Science.
  • 75% of officials at banks with over $100 billion in assets and 46% of officials at banks with less than $100 billion assets say they're currently implementing AI strategies
  • Major Banks in India are already leveraging AI to bridge the gap between humans and machine intelligence to procure robust business solutions in the domains of retail/conversational banking and process automation.
One of the major uses of AI & NLP is in Chatbots, the following are some of the major Indian banks that use Chatbots.
State Bank Of India (SBI): SIA AI Powered Chat Assistant.
HDFC Bank: Eva AI based Chatbot.
ICICI Bank: AI-based chatbot, named iPal.

Axis Bank: AI-enabled app that uses natural language processing to enable conversational banking that helps consumers with financial and non-financial transactions, queries and product information.

Utilization of NLP is also useful when it comes to Stock Behavior Predictions and Portfolio Selection and Optimization.

The main goal of every investor is to maximize its capital in the long-term without knowledge of the underlying distribution generated by stock prices. Investment strategies in financial stock markets can be predicted with data science, machine learning and nonparametric statistics. The collected data from the past can be used to predict the beginning of the trade period and a portfolio. Thanks to this data, investors can distribute their current capital among the available assets. NLP can be utilized for semi-log-optimal portfolio optimization. Semi-log-optimal portfolio selection is a computational alternative to the log-optimal portfolio selection. With its help, the maximum possible growth rate is achieved when the environmental factors are uncertain. Data envelopment analysis can be utilized for portfolio selection by filtering out desirable and undesirable stocks.

Predicting time series for financial analysis is a complicated task because of the fluctuating and irregular data as well as the long-term and seasonal variations that can cause large errors in the analysis. However, deep learning combined with NLP outmatches previous methodologies working with financial time series to a great extent. These two technologies combined effectively deal with large amounts of information. Deep learning by itself is not a brand new notion. In the last 5 years, a great number of deep learning algorithms have started to perform better than humans at a number of tasks, such as speech recognition and medical image analysis. Within the financial domain, recurrent neural networks (RNN) are a very effective method of predicting time series, like stock prices. RNNs have inherent capabilities to determine complex nonlinear relationships present in financial time series data and approximate any nonlinear function with a high degree of accuracy. These methods are viable alternatives to existing conventional techniques of stock indices prediction because of the high-level of precision they offer. NLP and deep learning techniques are useful to predict the volatility of stock prices and trends, and also is a valuable tool for making stock trading decisions.

Another one of the major uses of NLP modeling comes in the form of analyzing Financial Sentiment.

Successful trading in the stock market depends upon information about select stocks. Based on this knowledge, traders can decide whether to buy, hold, or sell a stock. Besides analyzing quarterly financial statements, it’s essential to know what analysts are saying about those companies, and this information can be found on social media. Social media analysis involves monitoring such information within social media posts and selecting potential opportunities for trading. For example, news of a CEO resignation usually conveys a negative sentiment and can affect the stock price negatively. But if the CEO was not performing well, the stock market takes resignation news positively and it may potentially increase the stock price. Financial sentiment analysis is different from routine sentiment analysis. It’s different in both the domain and its purpose. In regular sentiment analysis, the objective is to find whether the information is inherently positive or not. However, in financial sentiment analysis based on NLP, the purpose is to see how the market will react to the news and whether the stock price will fall or rise.

We at OnFinance utilize the potential of AI & NLP technology to screen through 200+ news sources, 40+ community groups that can be accessed through our app to come up with real time news and analytics, Riskiness of the entity (be it Stocks , Cryptos , NFTs , etc), Projected Market Impact of the News Article, Entity Sentiment (6 buckets: From Excellent to Very Poor), Search and Filter any entity, news, event and indicator using our NLP, along with that our app also hosts few other cool features like Real Time Entity Price, Direct Integration with Stock Brokers ( like Zerodha ) and DEX (Decentralised Exchange), Bookmark your favorite insights for later use.

Phew, that's quite a list, isn’t it? Wondering about how we do all this?

THIS IS WHERE THE MAGIC BEGINS…

We at OnFinance believe in the dreams of aspiring India… and in order to help people fulfill their dreams and aspirations we at OnFinance play our part by adding value to their investing experience by easing the process of investing by bridging the gap between humongous Financial Information and Investments. As more and more retail investors want to manage their own money, we want to provide real time wall street/hedge fund quality insights to the public (retail investors) so that they get their hands on the essential analysis while making their investment decisions.

When it comes to financial entities like Stocks or Cryptos or NFTs the most important and major driving factor is sentiment.

While sentiment in markets is mass psychology, it can in fact be understood by analyzing what a large number of people are saying. We at OnFinance use our Natural Language Processing models to understand public sentiments, analyze stock behaviour and come up with great ideas . We further use this data along with the news article to provide you with a curated list of financial entities to invest in and we don't stop here, we also risk assess the investment for you and provide you with a projected market impact of it.

For you we go lengths to screen through-
  • Analyst Ratings
  • Bankruptcy
  • Business Concerns
  • Company Financials
  • Competition
  • Contracts
  • Corporate Action
  • Criminal Actions
  • Disaster
  • Employment Actions
  • ESG (Environmental, Social and Governance)
  • Financing Activities
  • General Business Actions
  • Government
  • Laws and Regulations
  • Legal Actions
  • Market Performance
  • Mergers and Acquisitions
  • Partnership
  • Product Development
  • Rumors
  • Robo News
  • Stock Activities
  • Stock Valuation
  • Taxation
  • Trade War

Covering almost every aspect of financial analysis which is beneficial for you.

To see our work in action , here is a proof of accuracy-

Now that you are familiar with the dynamics of the working of OnFinance we would like to wish you guys a happy, prosperous and exciting investing journey with OnFinance. While most people misconceive being successful in investing in stocks and digital assets to be an outcome of good luck, we at on Finance believe in the famous saying-