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NLP Use Cases in Finance

As there is so much textual information in the finance sector, financial entities

resort to software based on natural language processing to better process it.

These solutions are constantly developing and going live. This article describes

popular and new NLP use cases in finance.

What is NLP?

Natural language processing is the capacity of software to understand human

speech in voice and text. NLP is a part of AI.

Financial companies apply the capacity of machines to work with the text to find

and analyze data in their domain. They can search both in free unstructured data

and in their own repositories.

The companies utilize voice processing in smart means of voice communication.

In the finance industry, NLP can be used solely and in combination with other AI

models. In this case, NLP represents the basis for such tools as ML, big data, data

mining, and predictive analytics.

How AI solutions understand people

To make machines grasp people's language, developers train algorithms. When

generalized, these models go through 3 steps in their work.

1. Machines search requested information.

2. Machines process relevant data.

3. Machines interpret data into meaningful text/ voice using context.

Going through these steps, machines perform a number of important processes:

● Map input people’s speech into meaningful representations

● Analyze the building of sentences

● Analyze the meaning of words

● Extract the meaning of the text

● Deliver outputs

NLP’s capacity to extract requested patterns from free data and capacity to

interpret the raw textual information into meaningful insights represent the

backbone of AI search, analysis, and prediction operations in the finance sector.

NLP Use Cases

Intelligent document search

NLP-based solutions can find all relevant documents in free data. Financial bodies

can need them for grounded decision-making. These solutions work due to NLP’s

capacity to find patterns in large volumes of unprocessed data.

If the solution cannot find documents, this means that they are not available in

free sources, or do not exist.

Automate capture of earnings calls

NLP-based solutions can automatically capture earnings calls.

Once a fiscal year or once a quarter, a public company makes an earnings

conference call. They are aimed to inform the company’s investors about the

earnings of the business. Brokerage firms, mass media, and financial analysts can

be interested in them.

If they use NLP-based systems they can get the companies’ press releases, the call

dates, general financials, key leadership changes, product updates, and new

partners.

Automate capture of leaders’ presentations

NLP-based solutions can automatically bring to financial bodies presentations of

companies’ management.

Periodically, companies’ leadership makes presentations or reports about their

financial progress. Financial entities that employ AI systems can get many factual

and analytical data in numbers and charts.

They can get an understanding of the company’s profitability, visions, and high-

level project overview. This information can be interesting in terms of investment

and analytics.

Automate capture of acquisition

announcements

NLP-based solutions will serve a great service while automatically finding news

about the companies’ merges and acquisitions. Financial institutions can be

interested in the earliest information about the change of ownership of the

companies and structural changes.

Voice money transfer. Experience of Royal

bank of Canada

Royal Bank of Canada offers its clients a mobile application for voice money

transfer. It is based on NLP, activated by voice, and can transfer money, or pay the

bills. In this application, NLP is used to understand a client's voice and to generate

human voice feedback. This application uses Siri and works on smartphones with

ios.

Clients can send money in three steps

1. The client calls the name of the contact and the sum.

2. The client confirms the operation via this application.

3. The client finalizes the operation with Touch ID.

Voice control of funds is very handy for banking clients. They can have more

freedom in managing their finances. This raises customer satisfaction and loyalty.

The authentication process in this solution is also handier than manual filling in

passwords. It is built on voice recognition and NLP.

AI chatbots. Experience of Commonwealth

Bank

AI chatbots are very popular with big banks. In 2018, Commonwealth Bank

employed Ceba AI chatbot. It serves more than 6.2 million users.

“Ceba is available 24/7, can recognize approximately 60,000 different ways

customers ask for the 200 banking tasks and will eventually be able to tell

customers what they are spending their money on,” boasts the bank.

AI chatbots are built on NLP. This model can grasp the meaning of human speech

and generate it. First AI chatbots could only answer simple questions. But with

the development of technology, they can be upskilled to personalized financial

assistants.

Still, not all situations demand such a high level of a bot. Banks also need middle-

level AI chatbots who serve regular client’s incoming calls. They can answer

clients' questions, direct them on the company’s web and mobile resources, or

switch the client to an appropriate specialist.

Central banks and NLP

Central banks are responsible for the oversight and management of all other

banks. Commercial and retail banks send their reports to the central bank.

Though, the central bank does its own research.

NLP together with other algorithms helps identify gross financially hazardous

transactions. As its major function is to find patterns in vast unstructured data.

Mark context data in query results

NLP can highlight the requested context in search results. For example, financial

institutions can find all mentions of some policy, regulation, or event with their

financial impact as a context. In this case, the system will generate all mentions of

the query phrase and highlight the mentions with financial impact.

Analyze financial sentiments. Study of

University of Cornel

Financial sentiment analysis differs from sentiment analysis in, for example, retail

or other domains. It does not use computer vision to see the customers’ faces.

And obviously, it has a different purpose.

This type of analysis in the finance industry uses solutions based on NLP to find

financial news, and emotional, and factual reactions to it. Further, they can

forecast the market reaction to particular financial news in this environment. This

forecast has a major impact on many areas.

One of the novel findings in this field was developed at Cornell University. FinBert

offers financial sentiment analysis with pre-trained models. The authors suggest

that pre-trained language models do not need many labeled examples. And they

also can be trained on domain-specific data.

This means that developers can train and rather fast the army of NLP-based

machines for a particular client or clients. It will find and forecast financial moods

for them.

Understand figurative meanings of queries

With NLP, financial specialists do not need to guess what search engines consider

keywords or key phrases. Also, they do not need to know the words that the

majority of users input in search. NLP can understand what they mean when they

speak in neologisms and different figures of speech.

Prediction of stock fluctuations

Based on financial sentiment analysis and prediction of market reaction to

financial events AI solutions can predict financial consequences for companies.

For example, if the stock price of the companies will fall or rise.

NLP for digital and challenger banks

Digital and challenger banks rely more on NLP in cases where physical banks can

utilize traditional means. For example, AI chatbots are the primary option for

these banks, not human assistants.

Introduce retention programs

In general, clients of the banks are not satisfied with their banking services, states

Entrepreneur reporting FIS study. It revealed that just 23% of clients feel happy

about their banking services.

NLP and AI solutions can offer specific retention programs for the banking sector.

Retention programs

1. AI solutions can include multi-channel support of the clients via sites,

social media such as Facebook, and mobile applications such as Whatsapp

and Viber.

2. AI solutions can do personalized offers.

3. AI solutions can collect client’s feedback while servicing them.

Risk assessments

Insurance companies are the first to utilize this function. They need to know the

history of the client to issue an insurance policy. Also, they need to know what

bills to collect and what to not. NLP can find all relevant information concerning

clients and claims.

All banks utilize risk assessment. Credit risk assessment raises the probability of a

successful loan payment.

Accounting and auditing

With the NLP and data analytics tools, financial entities can perform continuous

auditing of accounts and transactions. In this way, management can feel more

secure that they comply with accounting regulations and vet financial statements

in a proper way.

It is worthwhile mentioning that AI auditing solutions cannot substitute auditor

jobs. But they can rather upskill these jobs and raise the quality of the audit.

Segment customers based on their profiles

Companies can segment their clientele by having processed their account

information. For example, the structure of their income and expenses. Doing it

with NLP will take less time than the manual creation of filters.

Depending on their new segment, the clients can get specific information. For

example, they can be offered new products that match their new status.

Financial portfolio optimization

Financial companies endeavor to allocate their assets in such a way that will

preserve their best risk-reward balance. This means that they want to receive the

best ROI while not surpassing the planned risks.

Traditional financial portfolio

optimization

Smart financial portfolio optimization

In traditional portfolio optimization,

companies follow approximately this

scenario:

1. Financial entities include to their

portfolio diverse types of

investments such as mutual

funds and stocks.

2. Financial entities do

mathematical analysis and

target sums.

3. Financial entities employ hedge

strategies.

NLP with other models can offer a

different approach to solve this task.

1. First of all, smart solutions will

process free unstructured data,

find relevant information there.

2. Then, smart solutions will build

multiple inter-relations.

3. And afterward, smart solutions

will balance the risk-return ratio.

Track customer spending patterns

By applying NLP and big data, companies can create patterns of how clients spend

their money. With this information, they can forecast the volume of funds that

flows out per day or per month.

Offer personalized products

Personalization in offers is one of the main success factors in the financial

industry.

On the front end, banks can raise sales by providing personal AI-adjusted products

to clients via AI chatbots.

Using NLP, AI solutions can:

1. Collect history information about clients in free data

2. Leverage it with their account’s history, and the bank’s products

3. Create adjusted offers to individual clients

This can raise sales, and dramatically save man/hours of the bank's qualified

personnel.

NLP is massively used in the insurance business.

All insurance policies that insurance companies grant to their clients represent

personalized and AI-approved contracts.

For insurance companies, NLP is the first stage of the client’s history analysis. It

recognizes all relevant information about the client in free unstructured data.

Collect customer feedback

NLP is the basis of AI voice chatbots that serve the clients. They understand the

client’s speech and generate answers using human language. All interactions of AI

solutions with the clients are being recorded and stored. Specialists of financial

institutions can analyze them for better decision-making.

Key points

NLP and AI assist the financial industry in many areas of their work. NLP’s function

in finance is to extract requested patterns from free data and to interpret the raw

data into meaningful insights. This capacity represents the basis for all AI-human

relations in the finance industry. Due to NLP, AI solutions can do data collection,

analysis, alerting, and prediction. AI applications in finance can target from client

support to risk prevention. But the strategic AI applications specific to finance are

financial sentiment analysis and financial portfolio optimization.