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AI in the Banking Industry

Josh B

3 years ago

AI in the Banking industry | Insideaiml
Table of Contents
  • Introduction
  • Introduction to Natural Language Processing
  • Human-Computer Interactions
  • Understanding, Processing, and Generating Language
  • Natural Language Processing is also used for Artificial Intelligence
  • How Banks Use Natural Language Processing?
             1. Intelligent Document Search
             2. Investment Analysis
             3. Real-Time Event and Risk Detection
             4. Customer Service & Insights
             5. Check balances and transfer money
             6. Help customers manage recurring or late payments
             7. Key Takeaways for Banking Executives


          Artificial Intelligence which is also called natural language processing (NLP) and is being used by the branch to automate document processing, analysis, and customer service activities. Three applications include:
  • Intelligent document search -Finding relevant information from the large number of scanned documents.
  • Investment analysis - routine analysis of earnings reports and news so that analysts can focus on alpha generation.
  • Customer service & insights - Using chatbox to solve queries of customers faster.
There are lot of examples of how banks are using AI to increase their efficiency but let us first understand what natural language of processing is capable of.

Introduction to Natural Language Processing

Natural Language Processing | Insideaiml
          Natural Language Processing (NLP) is a branch of Artificial Intelligence enables computers to understand human language and respond in kind. This involves training computers to process speech and text and interpret the meaning of words, sentences, and paragraphs in context.

Human-Computer Interactions

          We input speech or text  (e.g. typing into a chatbot interface or talking to a smart speaker) then the computer converts the text/speech into a format it can understand (e.g. speech to text and words are converted to vectors). This helps computers cluster and classify different words.
Using its own data sets the computer figures out meaning and context. The computer determines an appropriate response and converts it to speech or text that we understand, and responds to us.
Every day we interact with apps that use natural language processing in Google Translate, we input text and speech that Google translates for us.
Gmail Smart Compose: Gmail completes the rest of the sentence you are about to type This feature uses the email subject and previous emails to suggest relevant text.
Grammarly: Grammarly which checks the sentence structure and grammatical errors in your article, it is proved to be better than Microsoft World word heck
Smart speakers: No, your conversation with Alexa isn’t magic (sorry).

Understanding, Processing, and Generating Language

          Natural language processing is actually includes two related methods:
  • Natural Language Understanding
  • Natural Language Generation.
1. Natural language understanding (NLU) figures out the meaning of the provided text and speech. it is similar to reading or listening. the process involves taking unstructured text and speech input from humans and converting it to structured formats that computers understand. When you ask Alexa for a weather report, for example, it uses natural language understanding to figure out what you’re saying.
2. Natural language generation (NLG) refers to computer-generated text and speech. NLG turns structured data into text and speech that humans understand. Continuing our previous example, Alexa uses natural language generation when it responds ‘It is sunny today. Would you like to place an order for sunglasses?’

Natural Language Processing is also used for Artificial Intelligence

          Natural language processing is often used with other AI methods such as neural networks, deep learning, and optical character recognition. Word2vec and Bag of Words are two popular natural language models
Without getting technical, neural networks are a subset of machine learning. They can process text, classify words, cluster similar words, and associate words and phrases with meanings when used for natural language processing. Deep learning methods (i.e. neural networks with many layers) such as Recurrent Neural Networks are also used.
Optical character recognition (OCR) enables computers to recognize text in scanned documents. OCR can be used with natural language processing to analyze scanned documents or handwritten text.
Various natural language techniques are used to determine grammar rules and word meanings. Syntax analysis involves determining grammar rules for words and clusters them according to similarity. The semantic analysis involves deriving meaning and is used to generate human language. Semantic analysis is challenging because human language rules are complex. Words and phrases take on different meanings in different contexts. Colloquialisms, idioms, and sarcasm further complicate matters.
Bag of Words and related algorithms are popular natural language techniques that classify phrases and document by category or type. Bag of Words simply counts how often each word appears in a document (a tally). The algorithm then compares documents and determine the topic of each document. This can be used to train neural networks. Gmail’s Smart Compose (mentioned earlier) uses Bag of Words and Recurrent Neural Network models according to Google. Search engines also use these techniques.
Word2vec is another popular natural language model. It is a two-layer neural network that classifies text to determine to mean. It converts words to mathematical ‘vectors’ that computers can understand. Vector conversion is required because neural networks work better with numerical inputs.
Vectors representing similar words are grouped together — similar words are mathematically detected. Properly deployed, Word2vec can infer word meanings with high accuracy based on past appearances. This is useful for document search, sentiment analysis, and even recommendations of which words should come next to complete a sentence.

How Banks Use Natural Language Processing?

Banks Use Natural Language Processing | Insideaiml
           Banks can apply natural language processing to large volumes of text and speech data to extract information, gain insights, and streamline manual tasks. While time and cost savings are obvious benefits, the ability to identify key information (the proverbial needle in the haystack) can be a competitive difference-maker.
Here are three areas where banks are applying natural language processing.

1. Intelligent Document Search

          JP Morgan Chase’s COIN (Contract Intelligence) software uses natural language processing to help the bank’s legal team search and review large volumes of legal documents.
COIN can reportedly save the bank’s legal team 360,000 hours, or 15,000 days, of document search tasks per year. It can extract key data and clauses to help loan officers review commercial loan agreements, for example.
COIN is apparently trained to recognize key information (attributes) within documents that the bank’s legal team flags as important. This enables the software to extract key information from documents that are structured differently. The bank claims it extracted 150 relevant attributes from 12,000 commercial credit agreements in seconds.
The software’s workings are not public since is used internally. We can speculate that it could be powered by natural language processing (to search within documents), optical character recognition (to recognize characters in scanned documents), and machine learning (to classify & cluster data within documents and to improve search algorithms over time).
These methods can be applied to other banking activities. It can help banks extract types of customer data that they don’t have time to track. This data could help predict customer needs and identify cross-selling opportunities. It can also speed up Know Your Customer (KYC) processes that require document analysis, thereby making customer onboarding easier.

2. Investment Analysis

          Securities research desks at banks are using natural language processing to find valuable insights within mountains of company reports and conference calls.
Banks previously hired armies of analysts to comb through earnings reports and other filings and enter pertinent data into databases and valuation models.
Now, banks are using natural language processing tools that ‘read’ hundreds of documents at a time and summarize key information for human analysts. Speech analysis tools can ‘listen’ to analyst conference calls to determine the tone and sentiment behind what company management is saying, which can provide insight for equity analysis. These tools are huge time savers and allow analysts to focus on alpha generation.
Banks also use natural language processing for sentiment analysis. These tools analyze large volumes of news and social media posts to extract key insights, determine how a company is perceived, or track market reaction to significant events. These timely insights can inform analyst recommendations.
Banks either use tools developed internally or by vendors. One vendor, Dataminr, claims to analyze social media and financial news to identify relevant information including unexpected news, emerging trends, or risks.

3. Real-Time Event and Risk Detection

          Dataminr's clients are the first to know about critical events and breaking information, enabling them to act faster…
On the sell side, natural language generation tools automatically generate reports based on earnings reports and news.

4. Customer Service & Insights

          Major banks are introducing some level of customer service automation through chatbots. In early 2019, Bank of America launched Erica, a mobile virtual assistant, which soon amassed over one million users through the BofA mobile app.
Erica accepts voice and text commands and combines predictive analytics with natural language processing to help customers:

5. Check balances and transfer money

          Searching for past transactions and account info on demand. Track spending habits (probably using predictive analytics, which is a value-add that encourages more chatbot usage)

6. Help customers manage recurring or late payments

          Chatbots let customers access account information and perform basic transactions on their phones instead of using internet banking or visiting their local branch. Executing transactions through a clean chatbot interface may also take less time.
A bigger win for banks will be using natural language processing for customer insights. Using methods related to intelligent document search and sentiment analysis described above, banks can better understand and predict customer needs and pain points. Sentiment analysis tools can monitor social media to see what people are saying about the bank. Document search tools can analyze feedback forms and customer information to respond to issues, offer tailored products, and increase customer retention.

7. Key Takeaways for Banking Executives

          Banking leaders realize that natural language processing can automate routine document analysis, research, and customer service.
Cost savings are just the tip of the iceberg. By analyzing text and speech data more quickly and extracting more actionable insights on customers and the market, banks can serve customers better and make better investments. The potential for greater market share and income is the real difference makers.
While we have not covered all possible use cases, banks can apply natural language techniques to any function that processes large volumes of text or speech data. There are numerous applications in compliance, risk management, or order execution, for example.
Key considerations include whether to build AI and natural language processing tools in-house or to license software from an AI vendor. Building in-house requires data scientists, developers, and an organizational AI strategy. While this takes time, internally developed solutions might meet the bank’s needs better than a vendor product. In addition, data quality and availability across departments also have to be addressed.
Given the broad range of banking activities that natural language processing can be applied to, banks that apply these solutions across departments are likely to see far greater returns on investment.
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