Natural Language Processing - University of Alaska Language Natural language processing

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  • 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

    https://content.gamalon.com/download-your-white-paper-idea-learning-defined?utm_campaign=WP%20Idea%20Learning&utm_source=Tech%20Emergence&utm_medium=white%20paper

  • 4All 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 “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 wi