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
veri
ng C
usto
mer
Per
cept
ions
Wit
h Su
rvey
Ana
lyti
cs
USE
-CA
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.
D
isco
veri
ng C
usto
mer
Per
cept
ions
Wit
h Su
rvey
Ana
lyti
cs
USE
-CA
SE 2
, CO
NT.
USE
-CA
SE 3
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
Cus
tom
er
Inqu
irie
s W
ith
Cha
tbot
s
USE
-CA
SE 5
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.
Solv
ing
Rou
tine
Cus
tom
er
Inqu
irie
s W
ith
Cha
tbot
s
USE
-CA
SE 5
, CO
NT.
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
12All 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.
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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