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1 All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019. Natural Language Processing Improving the Customer Experience in Finance, Insurance, and Banking A collaboration between Gamalon and Emerj

Transcript of Natural Language Processing - University of Alaska system Language Processing.pdfNatural language...

1All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019.

Natural Language Processing

Improving the Customer Experience in Finance,

Insurance, and Banking

A collaboration between Gamalon and Emerj

2All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019.

AI and machine learning are already driving

value for banks, insurance firms, and other

financial institutions by way of business intel-

ligence applications and process automation.

Natural language processing (NLP) is a ma-

chine learning approach that involves a soft-

ware “understanding” the intent and context

behind written and spoken-word words and

phrases translated to digital formats.

The advent of NLP use cases in finance, such as chat-

bots and conversational interfaces, has seemingly also

driven up the interest in NLP. NLP could allow a com-

pany to garner insights by summarizing documents or

gauging brand-related sentiment across the web.

More often than not, large businesses find it challenging to

unearth new insights from customer support. For instance,

a large insurance firm might receive millions of text-based

messages every year in the form of customer feedback or

interactions during customer support activities.

Large firms may find it’s difficult to have human employ-

ees crawl through customer data to identify key customer

issues at scale. The sheer volume of these incoming mes-

sages makes it difficult for banks to consistently leverage

insights that might be gleaned from customer data.

For businesses that are looking to garner insight from

their millions of historical customer interactions, NLP

and machine learning techniques could help automat-

ically discern what customers might be talking about.

We spoke with Peter HooPes, VP WW sales at Gamalon, Inc., who laid out some of the value that NLP

might bring to the customer experience in industries

such as banking, insurance, and finance.

According to Hoopes, machine learning and NLP can

help extract insights from what customers are talking

about when interacting with support representatives,

commenting on social media, or filling out customer

satisfaction surveys.

Natural language processing will likely transform the way

customers interact with large banks and insurance firms,

and NLP can help financial institutions of all kinds search

their volumes of digital historical documents, such as:

▪ Customer support tickets

▪ Customer surveys

▪ Trader-client emails or call transcripts

There are numerous other use cases for NLP in fi-

nance, but this white paper will focus specifically on

the benefits that NLP can bring to banks, insurance

firms, and financial institutions by way of improving

their customers’ experiences. These benefits include:

▪ Alerting the right departments of trending cus-

tomer issues, such as letting the product devel-

opment team know when customers are upset

with a product

An Introduction

NATURAL LANGUAGE PROCESSING

3All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019.

▪ Discovering and solving trending customer sup-

port issues in real-time before they get bigger

▪ Transitioning from brick-and-mortar banking to

digital banking to attract millennial customers

▪ Implementing chatbots to solve routine custom-

er service requests

▪ Ensuring regulatory compliance on the part of

customer support employees

▪ Making sure that traders and analysts are pro-

viding customers with useful, good financial ad-

vice in accordance with the law

We’ll discuss each of these benefits with examples in

banking, insurance, and finance broadly, but they are

applicable to all three of these areas equally and can

be applied to a number of different use cases.

First, we’ll begin with an overview of how NLP works.

HOW NLP WORKS

In order to get a machine to categorize customer sup-

port messages into certain “buckets,” for example, data

scientists can generally take two approaches:

1. suPerVIsed learnInG, or deeP learnInG

2. unsuPerVIsed learnInG

Deep learning requires subject-matter experts to label

vast quantities of data before its fed to the machine

learning algorithm. For example, if an insurance firm

wanted their NLP software to be able to categorize a

customer support ticket as “filing a claim” or “policy

question,” they would need to have experienced cus-

tomer support staff label some messages as “filing

a claim” and other messages as “policy question.”

This process can get even more granular, where sub-

ject-matter experts label some words and phrases as

certain categories or as synonyms of other categories.

Afterward, the labeled data is run through the NLP al-

gorithm, and the software would “learn” what consti-

tutes a “filing a claim” message and what constitutes

a “policy question” message. This process can take

considerable time.

According to Hoopes, NLP software can look at patterns

such as common words used in the beginning of a sen-

tence or words used together in several sentences to

categorize new messages automatically.

Some NLP software, such as that offered by Gamalon,

work a little differently. Instead of people labeling

messages as “filing a claim” or “policy question,” the

algorithm behind the software works by way of unsu-

pervised learning.

Unsupervised learning doesn’t require people to label

messages before they’re fed into the machine learning

algorithm. Instead, the algorithm runs through raw text

data and categorizes messages itself. Afterward, human

subject-matter experts can tweak the categories that the

algorithm creates, which continues to train the algorithm.

Gamalon calls this approach Idea LearnIng.

An Overview

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THE “BLACK BOX” OF MACHINE LEARNING IN FINANCE

NLP FOR CUSTOMER SERVICE IN FINANCE

Some vendors offer NLP software that are already tai-

lored to a particular industry, but the inner working of

the algorithms might be somewhat unclear. Financial

firms that need a quick integration might choose such a

vendor, although it might come at the price of not being

able to fully understand how the software is coming to

the conclusions it is.

More often than not, AI vendors today offer NLP soft-

ware that is a “black box.” The software might take in

data as input and the algorithms might be tweaked to

calculate a desired output, but it is very challenging to

understand each step in the decision-making process

of the algorithm. Additionally, modifying the software

to account for a new data category would require data

science expertise, time and resources each time.

Some vendors, such as Gamalon, claim their software

could be an alternative to the “black box” problem,

allowing non-technical experts to edit the algorithm

through their Gamalon Studio UI.

With an understanding of NLP software, we can then dis-

cuss its applications in finance, banking, and insurance.

Businesses in the finance sector have histori-

cally collected vast amounts of data about cus-

tomers, financial transactions, and markets, in

many cases due to regulations. This includes

customer interaction records for incoming

calls, email, text messages or social media

chat transcripts.

Large financial institutions have millions of customer

service tickets coming in from customers across the

globe. Each of these tickets could be relevant for one

or more internal departments within the firm.

Additionally, these customer service requests come

in through a variety of communication channels; for

example, customers could be calling in or filling out a

customer service form on a website.

Financial institutions collect data in the form of free-

form text in customer support tickets and call tran-

scripts in which customers describe their issue. The

sheer scale of the incoming requests makes it difficult

to read through each ticket manually and take action.

NLP and machine learning software could help with

automatically having a computer analyze these custom-

er service messages and categorize them, and ideally,

predict a next best action.

An Overview

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For example, let’s say JPMorgan receives large vol-

umes of customer service requests from customers

all over the world. NLP software could in theory help

a bank sift through all the customer messages and

automatically identify the top customer concerns.

The software might find, for example, that customers

have been comparing a certain product to another

brand often in the last month. The software could then

route the tickets to the product or marketing teams.

The challenging aspects in extracting genuine in-

sights about customer satisfaction might lie in de-

veloping NLP algorithms that understand context

when reading customer messages. For instance, if

a financial institution wants to understand how cus-

tomers feel about their wealth management team

by reading customer feedback, the NLP algorithms

might need to be tweaked to “understand” certain

financial terms and common trading jargon.

This might well be a much harder task than it ap-

pears, especially because customers might ask for

the same request in several different ways. NLP al-

gorithms can be trained to identify and categorize

these customer inquiries automatically. If a signif-

icant number of the messages had sentences that

started with variations of the phrase “my spouse,”

such as “my partner” or “my husband/wife,” NLP

algorithms can be trained to categorize such inqui-

ries the same.

It is also important to note that the accuracy of most AI

software capabilities are measured up to a reasonable

“level of confidence;” the software might not be capable

of performing a task accurately 100% of the time.

For an NLP software being applied to categorize a

large volume of documents, the algorithm might have

to be adjusted to account for edge-cases where the

software might be unable to make a decision.

For example, if a large financial firm uses NLP soft-

ware to automatically label and extract the top cus-

tomer complaints from open-ended forms on their

web portal, the software might be trained by using

datasets of labeled sentences.

The software might identify that terms similar to

the word “rude” were commonly associated with the

complaints for a customer service team in a partic-

ular branch, thus allowing the financial firm to take

action and introduce better training programs for

that branch.

Another option might be to use human experts to

train the software to identify more complex associa-

tions faster. The software can then start automatical-

ly categorizing new customer messages by predicting

a probability score for each category by analyzing

relevant parts of each customer message.

Considering the case where a customer complaint

message said the online trading platform did not

have a good interface, but the customer service

reps were very helpful in resolving the issue, the

software might identify positive and negative senti-

ments in the sentence, but human subject-matter

experts might be better at identifying which internal

department category needs to be added onto parts

of each sentence.

There will also exist cases where the software has

a low probability of predicting the correct ideas for

each message. Having human financial subject-mat-

ter experts categorize these “low confidence cases”

might allow the software to “learn” to identify and

categorize more accurately.

6All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019.

The insurance industry has historically been

dominated by large, slower-moving enterpris-

es. However, in recent years, AI uses cases in

insurance have gained traction, likely due to

access to large volumes of customer data and

resources in the industry. A few forward-look-

ing insurance firms seem to be applying AI

to customer analytics. According to Hoopes,

correctly identifying and extracting customer

insights from this mountain of data is harder

than it might appear, especially when training

a computer software to understand the way

humans communicate.

For a company with a large insurance customer

base that interacts with multiple product types

across communication channels, the difficulty is

compounded.

NLP and machine learning could allow insurance

firms to take large volumes of textual customer in-

teraction data and find patterns within it to catego-

rize and cluster these messages.

According to Hoopes, a big factor contributing to why

insurance firms might invest in AI for customer service

is that customers have most of their interactions with

insurance firms when they have an issue, when some-

thing has gone wrong.

Hoopes believes that businesses that have low lev-

els of interaction with their customers will find it

most attractive to start NLP initiatives to improve

customer service.

He explains the challenges in garnering insights from

customer feedback with a health insurance example.

If a large health insurance firm received a million

customer messages about claims applications or as

feedback, one challenge it faces is to identify which

phrases are referring to the same things. Hoopes

says insurance customers might say the same thing

in many different ways:

NLP FOR CUSTOMER SERVICE IN INSURANCE

“Let’s say a business receives a million mes-

sages from customers every year, across many

channels. Assuming that 50% of these people

are looking for a doctor, there might be 500,000

ways in which people are asking for a doctor.

For example, a customer might use the terms

I’m looking for a doctor, I’m looking for a spe-

cialist or I’m looking for a medical expert, all

of which mean that the customer is looking for

a doctor.”

7All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019.

A

lert

ing

the

Rig

ht D

epar

tmen

ts

o

f Tr

endi

ng C

usto

mer

Issu

es An insurance firm looking to read through

a large number of customer service tickets

could use NLP to automatically categorize

the top issues customers are facing and map

action-items to relevant departments. The AI

software can run through the customer ser-

vice tickets and list the things that are being

talked about the most, along with further de-

tails about each issue.

For example, the NLP algorithms might iden-

tify that several customers have mentioned a

particular insurance product alongside terms

that signify negative sentiment. Additionally,

the software might also identify that the word

“price” was often used in complaints about

that product. The insurance carrier can then

identify the product that customers don’t like,

which in this case might be due to its price, to

alert the product or marketing team.

There could also be a case where a customer

calls in for feedback and states that the cus-

tomer service representative was rude, but the

online experience was good. In this case, the

insurance carrier may want to contact the call

center manager to check on the representative.

In such cases, identifying how messages that

contain both positive and negative sentiments

about two or more different concepts might be

challenging for NLP algorithms without a sig-

nificant amount of either labeling or tweaking

by subject-matter experts depending on the

approach taken to train the algorithm (super-

vised or unsupervised).

In the insurance sector, businesses might not

have regular interaction with their customers

through customer support channels. Hoopes

explains this with an example:

“If you use an internet connection and it fails,

the internet providers are aware that the cus-

tomer is facing an issue. It’s possible to find

out about customer perceptions a lot easier.

“In insurance, businesses might not have

much information on how customers are

talking about their products. Auto insurance

providers might see the transactions on a

customer’s services account, but might need

to survey the customer to find insights about

what the common customer perceptions to-

wards brands and products are.”

As such, many insurance large insurance

firms will have backlogs of survey data from

which employees are unlikely to be able to

glean actionable insights; there are simply too

many surveys to sift through.

According to Hoopes, insurance firms could

make better sense of their survey responses

through the use of NLP and machine learning.

For example, an auto insurance firm may only

interact with its customers when they file a

claim or when the customer reaches out for

D

isco

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Per

cept

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Wit

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Ana

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USE

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SE 1

USE

-CA

SE 2

8All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019.

customer support. To remedy this, they might

collect data about what customers think about

their brand and product through open-ended

text surveys that they send to customers.

If they fail to anticipate just how many surveys

they’ll receive, they might find that the cost to

sift through all of them to garner any mean-

ingful insight is too much to bear.

Given a large number of survey responses, the

auto insurance firm could use unsupervised

NLP to automatically categorize these respons-

es. Subject-matter experts could then review

the categories to get a general sense of how

customers feel about their brand and products.

If they have on-staff data scientists or are

working with a vendor that allows for it, they

could also tweak the algorithm so that it cate-

gorizes responses the way they want it to.

Hoopes states that Gamalon worked with a

major health insurance player to identify the

top customer issues from millions of custom-

er survey messages.

According to Gamalon, the health insurance

carrier was facing challenges in gaining valu-

able insights from their survey responses due

to the large volumes of survey data. The exist-

ing rules-based survey analysis tools seemed

to be failing on delivering insights.

The vendor worked with the insurance firm to

analyze the customer feedback collected by

the firm. The data was in the form of unstruc-

tured open-ended customer survey respons-

es. Gamalon claims they helped the health in-

surance firm categorize the survey responses

in a way that provided actionable insights for

the sales team.

A supervised learning NLP approach could also

work for such a scenario, but it would require

time spent beforehand on labeling survey re-

sponses as the categories that the insurance

firms wants the algorithm to sort responses into.

Pre

vent

ing

Tren

ding

Cus

tom

er

Is

sues

Fro

m G

etti

ng B

igge

r NLP might also allow insurance firms to moni-

tor incoming customer data in real time to help

identify issues and take action before they affect

a large number of customers.

For example, a large health insurance firm

might be able to use NLP software to automat-

ically categorize customer service tickets into

buckets. As a result, customer service manag-

ers might notice that over 25% of the complaints

categorized in the last one month were about

login and password issues, for example.

The company could then proactively take ac-

tion and alert their IT team before the issue

affects more customers, essentially giving

insurance firms the ability to prevent prob-

lems from compounding.

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cept

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9All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019.

NLP FOR CUSTOMER SERVICE IN BANKINGTr

ansi

tion

ing

Aw

ay F

rom

Bri

ck-a

nd-

Mor

tar

Ban

king

: Att

ract

ing

Mill

enni

als In recent years, there seems to be a sense

of urgency for banks to go digital and expand

into new communication channels. In ten

years time, physical brick and mortar bank-

ing might not be the preference of the major-

ity of customers. To attract younger millen-

nial customers, banks seem to be realizing

the need to understand their preferences and

interact with them in the way they want to be

communicated with.

New digital communication channels, such

as chatbots and virtual assistants available

through banking web portals or mobile apps,

seem to be gaining popularity among the mil-

lennial customers. NLP plays a key role in

making these channels work.

Hoopes laid out some of the value that it

might bring to customer service in banking

when he said:

“We see a lot of customers wanting to talk

over free form text, social, voice or text, chat

messengers. The whole ‘Google effect’ means

that customers don’t want to talk to a person

anymore or don’t want to visit the branch.

Research firms like Gartner and IDC seem to

agree that in the near future a majority of the

new-age customers will want to communicate

in natural language voice or text to computers

and expect the systems to understand them.”

Banks might need to adapt to the new ex-

pectations of younger customers, who might

not want to visit the branch unless absolute-

ly necessary. Some of the larger banks have

been forerunners in adopting new commu-

nication channels, and many of them have

launched chatbots, virtual assistants, or con-

versational interfaces of other kinds. For the

larger banks, this poses an additional chal-

lenge of having to assess millions of mes-

sages coming in from additional communi-

cation channels.

Apart from these channels, most banks also

keep record of more traditional customer inter-

actions that happen over calls, texts, or website

forms. Banks seem to be collecting increasing

amounts of such data, which further supports

the idea that banks might focus less on main-

taining physical branches in the future.

That said, banks that have launched new cus-

tomer communication channels will also need

to adapt to new regulatory compliances rele-

vant to chatbots and conversational interfac-

es. Banks might need to monitor and analyze

customer complaints to identify cases where

the bank may be at fault, thereby potentially

breaking regulations.

Today, with customers leaning toward digital

banking, a large bank might receive millions

USE

-CA

SE 4

10All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019.

Tran

siti

onin

g A

way

Fro

m

Bri

ck-a

nd-M

orta

r B

anki

ng of customer messages through online por-

tals, and it might be almost impossible to

read through all these messages manually

and identify issues that customers seem to

be facing. Processing this volume of the in-

coming messages could cost a considerable

amount of human effort, time, and resources

to analyze.

NLP can also help banks manage the new

communication channels (conversational in-

terfaces) by automatically reading through a

large number of customer interactions. Fur-

thermore, NLP-based software might help

banks identify and prioritize customer com-

plaints that might need action from their reg-

ulatory compliance team. USE

-CA

SE 4

, CO

NT.

One of the more common applications for

NLP is in customer-facing chatbots and con-

versational interfaces. Most banks seem to

be phasing out of large scale brick and mor-

tar operations, and conversational interfaces

might be what a majority of banking custom-

ers prefer in the near future.

There seems to be a general dissatisfaction

among customers about conversational in-

terfaces and their ability to accurately deliver

useful information or respond to queries by

“understanding” the context of the conversa-

tion in a way that humans could.

Banks will need their conversational interfac-

es to improve in order to meet customer needs

without needing to escalate the customer to a

human customer service representative.

Hoopes considered the example of chatbots

to explain what might be driving the need for

better NLP capabilities:

“When we look at the early chatbots, such as

those developed by most large banking finan-

cial firms, they get criticized over being un-

able to respond to clients accurately. People

are underwhelmed by what chatbots can do

and get frustrated with the interface as they

feel they are not getting their voice heard.”

Hoopes noted that customers right now tend

to speak or type to chatbots robotically. If a

customer were inquiring about their account

details over a call with a customer service rep,

they might say, “Can you tell me my account

balance?” In this case, customers might sim-

ply say “Need account details.”

Understanding that both these requests

mean the same thing is easier for humans

with financial context. Allowing humans to

categorize these types of customer inqui-

ries might help accelerate the “learning” for

NLP algorithms.

In this case, a banking subject-matter expert

might indicate a list of such phrases that all

might essentially mean the customer wants

details on their account. Periodically allowing

these experts to add more of these word or

Solv

ing

Rou

tine

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ith

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s

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11All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019.

phrase associations might help the algorithm

improve the accuracy of categorization.

It is possible that the NLP algorithms might

discover these associations automatically, but

this could require much more training data

and time than if a human expert helped the

algorithm categorize the requests.

However, Hoopes also stated that not all the cat-

egorizations require a human in the loop. For

example, if a financial firm engaged with cus-

tomers through a social media messaging plat-

form, the algorithm might identify the sentiment

in a particular message by detecting the use of

commonly used positive or negative terms.

Such relatively simpler categorizations might

be in the realm of being completely soft-

ware-driven. Given enough examples to train

on, the software could automatically identify

pattern which patterns show up again in any

future customer interactions.

Hoopes states that while NLP algorithms can

automatically learn to identify how to categorize

messages, this usually involves massive amounts

of data and a long time to get the software to work

the way it is supposed to. Gamalon’s algorithm, for

example, categorizes messages on its own first.

Then, subject-matter experts at the bank tweak

the categories the algorithm comes up with.

The benefits that conversational interfaces

might bring to banking customers might make

it easier for them to access information or file

a complaint. These conversational interfaces

will get better over time and the number of

events that necessitate a branch visit from the

customer might decline.

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NLP FOR ANALYZING TRADER-CLIENT INTERACTIONS IN FINANCE

The finance industry has been an early adopter

of AI. It is likely that the use of algorithms in

trading and the fact that most large financial

firms already had teams of software develop-

ers aided the transition into data science and

AI applications in finance.

NLP might essentially allow large financial firms to

automatically read and categorize documents which

contain free-form text. Hoopes mentions the example

of a large finance firm which needed to identify cus-

tomer complaints that might involve cases of regulatory

non-compliance.

Financial advisory services are highly regulated and

recent changes seem to be leaning in the direction of

having the onus on financial firms to monitor the per-

formance of their advisors.

If a large financial advisory received a customer com-

plaint, such as charging a fee that was not clearly

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stated initially, the firm could be in violation of certain

regulations, attracting a fine or a bad regulatory rating

from supervisory agencies.

NLP software can run through several millions of in-

coming customer service requests to identify and cat-

egorize ones that need immediate action to avoid reg-

ulatory issues. The software can be trained to “read” a

message and automatically populate a table or schema

from the text to list out customer requests that need

action from the compliance team.

Hoopes gives the example of a financial services firm

trying to understand the interactions between traders

and clients. These companies need to identify broadly

what their traders are saying to their clients and what

advice is being given to each client.

There has been a recent regulatory push from supervisory

bodies towards ensuring that traders aren’t giving bad ad-

vice to clients. NLP can help understand these customer

interactions at a massive scale and gain insights such as

the ability to gauge the performance of financial advisors.

NLP and machine learning could be used to read

through transcripts of trader-client interactions and

identify which parts relate to any financial advice being

given. For example, if a trader says “I would advise you

to place a buy order for these trades,” the software

could be trained to label and tag parts of this sentence

in the context of financial advice.

The NLP algorithm would most likely identify the words

“buy” and “trade” in the context of a sentence beginning

with the phrase “would advise you to.” It would then

likely categorize it as financial advice.

Similarly the software might also help identify and

extract the parts of a conversation where the trader

is suggesting a trading action to the client. The firm

could read through these advisory messages from their

traders to detect any instances where the wrong advice

might have been given, possibly indicating the need to

improve trader training processes.

Additionally, Hoopes explains that capturing the trad-

er-client interaction from call or chat transcripts might

help with identifying fraud. For example, NLP-based

software might help financial services firms identify

that a particular series of sentences always show up

in conversation records for cases of fraud.

Using historical evidence and public datasets, fi-

nance firms can generate a list of common words,

phrases, or topics that are associated with fraud.

The NLP algorithms crawl through the messages to

identify sentences that contain any of these words

of phrases. The software might also automatically

cross-reference historical fraud cases and find pre-

viously unidentified patterns in conversation that lead

to cases of fraud.

A few vendors also offer software that allows finan-

cial institutions to dig deeper into their data and

identify undiscovered fraud patterns through diag-

nostic tools. These might be in the form of a dash-

board that allows the non-technical employees and

financial subject matter experts to edit the way the

algorithm labels.

Employees can also see ranked lists for certain phrases

or topics that mean the same thing and make additions

or changes. The algorithm learns to label sentences bet-

ter with more such inputs from subject matter experts.

Business leaders in finance might also need to be

aware of the range of capabilities of both NLP and

what a particular vendor can offer. The most common

13All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019.

approach to NLP software offered by vendors catering

to the finance sector seem to grant financial firms the

ability to summarize text by extracting parts of a doc-

ument that the software deems as useful.

For instance, JPMorgan announced they internally-de-

veloped a contract abstraction software tool called

COin, which uses NLP to automatically extract the

most useful portions of contracts.

Most such software also allow for a level of automatic

categorization of documents. The software “learns”

to categorize the document by reading through the

document and learning from examples categorized

by humans.

Hoopes noted that even among the NLP vendors

there might be levels to what capabilities are offered

for financial firms. Some vendors offer software that

can perform the extraction and categorization tasks

mentioned above. But the software might require

the expertise of data scientists whenever the algo-

rithms might need to be tweaked to accommodate

new data.

NLP FOR REGULATORY COMPLIANCE IN BANKING

With the emergence of chatbots and oth-

er conversational interfaces, the banking

regulations around the implementation of

these new communication channels have

also emerged. For example, the GDPR reg-

ulations state that banks that have imple-

mented chatbots need to define policies and

procedures for customer data protection,

assess potential data risks, and adhere to

codes of conduct.

Further adoption of conversational interfaces mean

that banks collect even larger volumes of customer

interactions. These new communication channels re-

quire banks to follow additional regulations. NLP-based

regulatory monitoring tools might offer a way for the

larger banks to manage new communication channels

and ensuring that banks are complying with mandated

regulations.

For example, customer complaints might contain cas-

es where a customer might claim compensation for a

fee that was wrongly charged by the bank. As Hoopes

explains it:

“Considering the case where a customer calls

in and says that the bank charged him an over-

draft fee that they shouldn’t have, since he has

overdraft protection. If it turns out that this is the

banks fault, they might be breaking regulations

leading to lower ratings from supervisory bodies.”

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Hoopes also quotes an example from a client his firm

worked with. The large bank had over 60 million cus-

tomer complaint messages coming in from several

channels every year. A small number of these com-

plaints, where the bank was at fault, needed to be iden-

tified in order to firstly resolve the customer’s issues

and also ensure that they didn’t attract any regulatory

fines or rating cuts.

The bank needed to read through all the messages and

understand their intents to categorize them as relevant

for immediate action. The bank worked with Gamalon to

develop an NLP-based categorization tool that helped

classify complaints with regulatory importance by using

input from the bank’s regulatory experts.

Banks might also find previously undiscovered pat-

terns to identify customer support tickets that lead

to regulatory violations. For instance, the software

might identify that customers who file a complaint

about misallocation of funds usually have a certain

type of tone and use phrases or words that might be

similar. Banking regulatory staff can then identify and

address more such customer issues earlier to avoid

any non-compliance.

Regulatory compliance is a persistent issue for banks

requiring constant monitoring to ensure that the bank

is adhering to local, national and international rules

for conducting financial transactions and handling

customer data. As regulatory requirements increase

(such the GDPR), the costs to serve an individual cus-

tomer often go up for banks and AI software might

help cut these costs down and allow banks to serve

more customers.

In a real world use case, Deutsche Bank claims they

developed an aI sOftware tO mOnItOr fInanCIaL reguLatIOns . Their software can purportedly

sort through large volumes of interaction data

between customers and employees to ensure the

bank’s employees are complying with rules and

regulations. The bank was finding it challenging

to meet regulatory standards since a majority of

their customers preferred communicating through

online channels and the volume of incoming

messages was too huge.

According to Deutsche, the NLP system can auto-

matically search for terms that compliance auditors

might look for, a task which previously meant manu-

ally going through tape and listening to several hours

of audio recordings.

A few of the incoming messages might contain pat-

terns in conversations that correlate to fraud or money

laundering cases. For instance, historical customer

conversations regarding fraudulent claims for sto-

len credit cards can be input to NLP-based software.

When the software finds new messages which have

been tagged as suspicious, it can alert the bank’s

fraud detection team.

15All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019.

NLP might help banks, insurance firms, and

other financial institutions search large vol-

umes of structured, semi-structured, and

unstructured documents and extract data

from them.

A lot of the business in the finance industry still gets

done on paper. Advanced optical character recognition

and computer vision software can now help financial

firms digitize these documents, allowing NLP soft-

ware to search them.

This could open up a new avenue for larger financial

firms to gather insights from sources that might have

been previously untapped due to the inability to gather

data recorded on paper.

In some cases even with digital data capturing sys-

tems in place, the large organizations cannot manu-

ally handle the sheer volume of incoming customer

service tickets coming in from a variety of channels.

The dichotomy of insurance firms, banks, and other fi-

nancial institutions having to deal with growing customer

interactions every year that they cannot handle and the

CONCLUDING THOUGHTS on NLP in finance, insurance, and banking

THE CHALLENGE OF ADOPTING NLP

A key friction for many businesses might be

that they can’t seem to identify every scenar-

io for customer search queries in chatbots.

Teaching a machine which has no precon-

ceived knowledge of human speech to make

associations like humans is an incredibly dif-

ficult task.

NLP and machine learning offerings today support

varying levels of visibility into the AI system. Many AI

vendors offer software that works on supervised learn-

ing. Once the algorithm is trained on labeled data, it’s

unclear how it “processes” that data, so to speak, to

come to the conclusions it does.

In other words, an NLP software might be able to cat-

egorize certain messages as a request for an account

balance, but there’s no way to really figure out why the

algorithm categorized the message that way.

Other NLP vendors offer software that can double up

as diagnostic tools and allow even non-technical sub-

ject-matter experts at banks to sift through customer data

and possibly tweak the NLP algorithms to suit their appli-

cation better. For example, Hoopes claims that Gamalon’s

systems allow users to rank and compare words that have

been categorized to mean the same thing or look at prob-

abilities for whether two sentences mean the same thing.

He adds that this can be done for millions of words and

phrases, and users can edit these lists to help improve the

accuracy of the algorithm in “understanding” and catego-

rizing free-form text messages.

16All rights reserved. This publication may not be reproduced or shared electronically or physically in any way without explicit permission from the publisher. Copyright © Gamalon and Emerj 2019.

fact that younger millennial customers expect a lot more

in the customer service department than older custom-

ers seems to be driving banks, insurance firms, and fi-

nancial institutions towards adopting NLP software.

NLP-based search software could be a key to allowing

financial enterprises access to their volumes of digital

and physical records, allowing them to search customer

support tickets and trader-client interactions at scale.

Banks, insurance and financial firms are right now

applying NLP to improve their customer service oper-

ations and inform product development. Any such en-

deavor still might be most relevant for medium to large

enterprises with sufficient access to data, capital, and

data science talent. Local banks and insurance firms

will need to wait a while before AI in general becomes

more available to them.

For enterprises looking to adopt NLP into their cus-

tomer service workflows, they will first need to figure

out the kinds of insights they are looking to garner

from their customer support data. To do this, they will

often need their subject-matter experts to work with

data scientists, either those that work at the company

itself or those that work at an AI vendor company.

Financial enterprises might work with a vendor that

provides an unsupervised NLP software that can start

categorizing customer messages relatively quick-

ly after purchasing the software, but such vendors

are rather uncommon. In these cases, such as with

Gamalon, subject-matter experts don’t need to label

data, but instead tweak the categories that the algo-

rithm creates on its own.

For the most part, NLP vendors offer supervised

learning, which requires an integration process that

includes subject-matter experts (in this case, likely

customer support staff at the insurance firm), to label

customer messages as certain categories. This would

train the algorithm behind the software to accurately

categorize messages itself.

NLP software might now be able to help large insur-

ance firms get a deeper understanding of what their

customers are most often talking about or having

issues with. It’s important, however, to begin AI ini-

tiatives with a clear objective. In this case, insurance

firms should know the kinds of insights they’re look-

ing to garner from an NLP software before they work

with an AI vendor or hire a team of data scientists to

build an algorithm.

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Gamalon helps you understand your

customers no matter where they’re

commenting, chatting or posting so

that you can take impactful actions

for your business. From 2013 to 2017

Gamalon received the largest single

contract for next generation of ma-

chine learning from DARPA. With

a foundational advance in machine

learning developed in collaboration

with leading groups at MIT, Berkeley,

Stanford, and Columbia, and over 40

patent filings, Gamalon was named

one of the 50 Smartest Companies by

MIT Technology Review in 2017, and in

2018 became a World Economic Fo-

rum Technology Pioneer.

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