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AI in the Banking Industry
Josh B
3 years ago
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
Introduction
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 (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 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…
www.dataminr.com
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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