Financial services companies around the world are getting into the recording business in a big way. No, they’re not going into the studio with the latest pop music sensations. Instead, they’re recording just about everything being said in and around their institution. Client trades made over the phone. Voice mails left for financial advisers. Conference calls. Audio from videoconferences. Employee calls from mobile devices, whether the company’s or their own.

A variety of federal, state and international regulations are driving institutions

to blanket their operations with voice recordings. Regulatory requirements

specify how long recordings need to be retained by financial firms and how

quickly records must be delivered to government agencies or other requestors.

Furthermore, compliance can involve granular searches. For example,

complying with e-discovery requests may include isolating a specific

recording of a certain employee or client conversation.

For some time, institutions have stored and analyzed the content of audio

communications using speech-to-text technology that translates audio into a

minable data format. But the recent, rapid growth in regulatory requirements

has thrown the limitations of speech-to-text into sharp relief. For instance,

a bank that previously only had to record 1,000 turrets of financial trader

information could now find itself having to record and retrieve audio from

half-a-million telecommunications end points throughout the enterprise.

Speech-to-text technology simply isn’t up to this kind of challenge, for several

reasons. As a result, institutions are increasingly turning to phonetic search to

find and analyze conversations and content. Phonetic search is a process built

on phonemes — basic language elements that provide the building blocks for

how human speech sounds.

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Phonetic search: A powerful new regulatory compliance tool for financial institutions

Complying with

e-discovery requests

may include isolating

a specific recording

of a certain employee

or client conversation.

Phonetic search can help financial services companies meet increasing

regulatory requirements by providing the capability to capture calls in real

time regardless of the source. Institutions can then use advanced analytical

tools to mine the phonetic records from those calls to identify specific topics,

specific people and specific calls.

Growing mandates and scrutiny

Two very recent developments highlight the increasing legal and regulatory

imperative that financial institutions be able to record and retrieve specific

calls and other audio exchanges.

In April 2012, the U.S. Commodity Futures Trading Commission (CFTC)

finalized regulations recommended in the Dodd-Frank Wall Street Reform

and Consumer Protection Act regarding reporting, record keeping and

daily trading records for swap dealers and major swap participants.

The regulations state that effective July 3, 2012:

Each swap dealer and major swap participant shall make and keep

pre-execution trade information, including, at a minimum, records of

all oral and written communications provided or received concerning

quotes, solicitations, bids, offers, instructions, trading, and prices,

that lead to the execution of a swap, whether communicated by

telephone, voicemail, facsimile, instant messaging, chat rooms,

electronic mail, mobile device, or other digital or electronic media.1

In March 2012, the U.S. District Court in New York granted a Federal Trade

Commission (FTC) motion for summary judgment against businessman

Paul Navestad for violating the FTC Act. Navestad was found to have made

1 Commodity Futures Trading Commission, 17 CFR Parts 1, 3 and 23 RIN 3038–AC96, § 23.202,

Daily trading records.

Phonetic search can help financial services companies

meet increasing regulatory requirements by providing

the capability to capture calls in real time regardless

of the source.

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While the Telemarketing

Sales Rule does not apply

directly to financial

institutions, individuals

or companies, it does

apply to them indirectly

when they contract with

an institution that must

comply with the TSR.

material, false and deceptive claims to deceive consumers and to have

violated the Telemarketing Sales Rule (TSR). In addition to calling consumers

on the national Do Not Call Registry, Navestad’s violations included:

• Not providing an opt-out mechanism for consumers not wishing to

receive calls;

• Not providing consumers with the ability to speak to a live operator; and,

• Making false and deceptive statements intending to induce consumers

to pay for services that would allegedly enable them to easily and quickly

receive public or private grants.2

While the TSR does not apply directly to financial institutions, individuals

or companies, it does apply to them indirectly when they contract with an

institution that must comply with the TSR.

However, two other rules do apply directly to financial services companies: the

Telephone Consumer Protection Act (TCPA) and the Gramm-Leach-Bliley Act.

The Federal Communications Commission recently amended the TCPA to

include telemarketing done by banks and insurance companies in Do Not Call

Registry rules and regulations and in the Gramm-Leach-Bliley Act, including

provisions that protect personal consumer financial information held by

financial institutions.

Gramm-Leach-Bliley’s privacy requirement has three principal parts: the

Financial Privacy Rule, the Safeguards Rule and pretexting provisions. Civil

penalties are steep, costing up to $10,000 per violation levied against officers

and directors found to be personally liable and up to $100,000 per violation

for financial institutions held liable.

These are just a few of the regulatory requirements that either directly or

indirectly impact U.S. financial institutions. Multinational firms need to add

foreign regulations to their list of concerns, such as those imposed by the

U.K.’s Financial Services Authority (FSA) requiring all participants in the

country’s capital markets to begin recording mobile communications,

including voice, short message service (SMS) and instant messaging (IM),

of all their employees involved in trading by November 2011.3

2 http://scholar.google.com/scholar_case?case=17476208810662567879.

3 Policy Statement 10/17, “Taping of mobile phones,” Financial Services Authority, http://www.fsa.gov.uk/

pubs/policy/ps10_17.pdf.

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Speech analytics solutions, in general, convert

recorded audio to text and then perform a text search.

This approach has several limitations associated with

efficiency, cost, propensity for errors and lack of flexibility.

In response, should financial institutions consider taking the approach of

recording all employee voice communications? Such a decision would have

monumental technical implications, especially if those firms are using

speech-to-text technologies for call retrieval and data-mining purposes.

The limitations of speech-to-text

Speech analytics solutions, in general, convert recorded audio to text and

then perform a text search. This approach has several limitations.

First, speech-to-text conversion is inefficient, consuming considerable CPU

and memory resources. The process effectively duplicates the content,

which once converted, must still be searched in its entirety. This both drains

resources and creates a content management challenge.

Also, the process of converting the spoken word to a text file requires that

a series of dictionaries be loaded into the conversion system. In addition,

the hardware- and software-intensive nature of speech-to-text solutions

makes their widespread deployment, in perhaps hundreds or thousands of

financial institution branches for example, an expensive proposition.

Speech-to-text is error prone. The further content is removed from its

original source, the more likely it is that errors have been introduced during

the conversion process.

Finally, speech-to-text offers limited flexibility. Words and phrases to be

searched in the converted text must be predefined in a dictionary of terms

for the text search engine to perform. For ad hoc searches, this can become

unwieldy and a challenge to manage.

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For any user wishing to

access the information

from an audio stream in

real time or cost-effectively

deploy the solution across

an enterprise, the phonetic

search approach is the

only practical option.

The phenomenal power of phoneme analysis

Until fairly recently, the science of phonetics was confined to university

research laboratories. However, the breadth of potential applications in

the commercial world is accelerating its development and use. For any

user wishing to access information from an audio stream in real time, the

phonetic search approach is the only practical option. Its lightweight

requirements in terms of the processing power required to perform a

search mean that it is able to scale easily to whatever levels are required

to cover an entire organization.

The benefits of this approach are wide-ranging, perhaps the most valuable

being its ability to reduce decision-making latency based on accurate and

up-to-date information. Real-time phonetic search enables insights discovered

in speech to be populated into business intelligence (BI) platforms, allowing

financial institutions to consume aggregated data, measure the scale of a

problem and compare its criticality to other issues — all within a very short

time from occurrence to discovery. This low latency then enables companies

to deploy proactive notification systems to make the technology work in an

observer-less way, saving time and resources.

Another benefit of phoneme-based searches is that they do not require a large

vocabulary of predefined phonemes. For example, there are 40 phonemes in

U.S. English and 44 in U.K. English. Bottom line, phoneme-based searching is

faster and more efficient than speech-to-text conversion and search.

The first step in phonetic search is to build a language- and dialect-dependent

index of the audio content represented as phoneme strings (Figure 1). Future

searches then leverage this index to yield hits or results. Words and phrases

a user searches on are converted into phoneme strings, and searches or

matches are then obtained by walking through the index. Each hit enriches

the context for future searches.

Results are presented in such a way that a user can see which portion or

region of the selected audio content contained phrases or utterances

deemed to be similar.

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Figure 1. Building a phonetic search

Audio data is transformed into phoneme strings

h e l @ U . . .

Hello

1

3

Words and phrases converted to phoneme strings and searched

Relevant search results are displayed

2

Taking phonetic search to the next level

Two recent technology advancements are expanding the capabilities of

phonetic search. One is the development of high-performance desktop

clients for searching and indexing searches in real time. Searches can be

issued through such a client, and, as part of the process, relevance thresholds

can be set that define results the user can ignore. There is no right or wrong

way to set the relevance threshold level. By varying it, trade-offs can be made

between false positives and false negatives.

The second noteworthy development in phonetic search is the emergence

of cloud-based BI solutions. Scalable, secure cloud-based BI platforms can

provide advanced analytics and reporting with low organizational risk, impact

and cost. For example, phone calls can be tagged by criteria, such as the

work shifts during which they occurred or the top reasons clients are calling

the institution.

Cloud solutions also offer automated upload of search and discovery results

to an analytics and reporting engine. They can also include out-of-the-box

coverage for industry-standard key performance indicators such as first-call-

resolution and average-hold-time analysis.

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These and other advantages of phonetic search not only can offer faster, and

potentially more accurate, analysis but also can significantly lower expenses across

an organization, particularly relating to the hardware platform required to deliver a

particular capacity of analysis. In short, what is accomplished on an entire server for

speech-to-text search can be accomplished on a single core for phonetic search.

For example, speech-to-text system analysis of 200 hours of data in 24 hours may

require the purchase of a server-grade computing resource that costs around $2,000.

A phonetic search system can analyze 500 hours of data in only two hours, requiring

only a laptop computer costing less than $1,000.4

Also, total cost of ownership may be lower due to the reduced maintenance effort

required to operate a phonetic search system. A phonetic search system is not

dependent on a dictionary to perform recognition, which means it natively supports

product names, jargon and other non-dictionary phrases. This translates into

fewer ongoing costs relating to system operation and support of a changing

business environment.5

A powerful compliance capability

Financial services companies are under great pressure to record verbal transactions

of every kind and to be able to retrieve recordings on demand. Phonetic search,

coupled with powerful desktop clients and cloud-based deployment, can help

financial firms respond rapidly to regulatory and other legal demands for the

content of conversations, transactions and other voice interactions.

1 Not including software, maintenance and other costs. Illustrative only, based on Avaya experience through

client engagements.

2 Based on Avaya experience through client engagements.

About AvayaAvaya is a global provider of business collaboration and communications solutions,

providing unified communications, contact centers, data solutions and related services

to companies of all sizes around the world. For more information, contact your Avaya

Account Manager or Authorized Partner or visit us at www.avaya.com.

© 2012 Avaya Inc. All rights reserved.

Unless otherwise noted, all trademarks identified by the ®, TM or SM are registered trademarks, trademarks or service marks,

respectively, of Avaya Inc.

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