Statement by Nick Ferens

Manager, US Civil Market

DeticaDFI

Before the House Agriculture Subcommittee on

General Farm Commodities and Risk Management

June 7, 2007

Chairman Etheridge, Ranking Member Moran, and Members of the Panel, Good
morning and thank you for the opportunity to appear before you today. My
name is Nick Ferens, and I am the Manager for the US Civil Market for
DeticaDFI ("Detica"). Before describing my company, qualifications, and
recommendations, I would like to provide my aspirations for appearing
here today.

I. Vision of the Future

The Risk Management Agency (RMA) has a difficult balancing job to do:
promoting and regulating solutions to keep our agriculture sector stable
and productive, while at the same time being good and effective stewards
of taxpayer resources. The Agency has adapted useful tools to help it
meet its mission, and it has partnered with well-regarded individuals,
entities and programs to obtain some of the resources that it needs to
do its job. But, in an age of changing technologies, rapidly expanding
amounts of data, and behavior that quickly adapts to changed
environments and circumstances, we need to think about RMA in a more
holistic way, rather than approach the process of detecting fraud, waste
and abuse in a linear but piecemeal fashion. We need to help RMA obtain
more resources---human and technological\-- to balance these needs and
to be good, informed consumers to make better technology decisions for
itself. Our view envisions an agency that has the technological
infrastructure that can power the intellectual underpinnings of fraud
detection, the internal workforce that can operate the infrastructure
and understand the inputs and outputs of the system, and a collaborative
process that builds and expands upon the successes that RMA has
accomplished to date. Through this comprehensive approach, we believe
the long term investment by RMA in fraud detection will be minimized,
and the return on investment optimized through:

1.  better oversight by and of the insurance agencies,

2.  reduced management burdens on all participants,

3.  better detection through efficiently targeted investigation and
    enforcement, and

4.  effective use of taxpayer resources.

II\. Professional Qualifications

DeticaDFI is a consulting organization that works with a wide range of
private and public sector organizations to convert data into actionable
intelligence. We provide a wide spectrum of data intelligence and
analytics services, with particular focus on the areas of fraud
detection, risk management, security, and regulatory compliance.
Although we are well known in the financial services arena, our roots
and domain expertise reside in the government/national security sector.
Collectively, we have more than 30 years' experience working with
various governmental entities.

Perhaps the easiest way of helping you understand what Detica does is to
provide an example. The Insurance Fraud Bureau (IFB) is a body
established in 2006 to detect and investigate serious and organized
fraud in the UK. The IFB was established because the insurance industry
needed to tackle distributed claims fraud. The insured in this example
would collude using a variety of techniques and make multiple fraudulent
insurance claims across multiple insurers. Detica applied a series of
advanced new data analysis techniques to detect patterns of fraudulent
behavior in large data sets. The combined data is over 26 million
records covering more than 32 million families. By combining multiple
data sources to form the "big picture", more accurate risk scores could
be generated and delivered to investigators to maximize their capacity.

Once we have helped our clients understand and articulate the problems
they want resolved and formulated a strategy to resolve it, we can then
offer a range of technological solutions as appropriate. These solutions
do not simply include data warehousing and data "mining", but include
the full range of predictive analytics, data quality assurance, web
integration, enterprise content management, text mining, search and
retrieval, and communications monitoring.

We live in a world where vast amounts of data are being generated by the
minute. It is beyond dispute that to detect fraud, one must be able to
understand the data and use techniques that will winnow out the good
actors from those that are bad. However, that is really only the
beginning of the story. In this day and age, fraud, waste, and abuse
(hereinafter "improper payments") scenarios are increasingly
sophisticated, being carried out less by individuals acting alone, but
through organized networks of people. The trick then is to use the data
to reveal these networks before the improper payment has occurred. In
this respect, Detica has been a pioneer in the development and use of
sophisticated software and techniques to detect these networks. I have
attached a couple of power point slides that will help you to visualize
how these networks appear. This is in sharp contrast to RMA's current
model, which is looking for individual anomalies.

In short, what we do, through our NetReveal solution, is use the
data---however sparse or voluminous it might be\-- to identify whether
there are linkages or connections between people. Once the linkages are
created, the customer, in this case, RMA, can then begin to understand
whether the linkages are meaningful in terms of suggesting potentially
wrongful behavior, and then further investigate those patterns and
linkages. The strength of the system is that it identifies networks, not
just individuals. Equally important, it helps better direct taxpayer
resources, not just to investigate large populations, but to focus
investigators where there is a statistically high probability that bad
behavior (by multiple persons) is occurring.

Besides the ability to identify networks and connections between people,
we help customers understand how their business processes may influence
behavior leading to other types of improper payments that are beyond
detection using traditional methodologies. Let me give an example.
During this subcommittee's hearing on the integrity of the crop
insurance program last June, Administrator Gould was asked whether there
was a cutoff as to which claims to pursue. Administrator Gould answered:

> "Well, typically the \$100,000 limit is where we separate the big ones
> from the small ones. The \$100,000 ones and above get our undivided
> attention. Those under that get attention, but it comes down to a
> matter of resources and time as to how aggressively we pursue those."

Because of that testimony, the Agency's business rules are now a matter
of public record. Individuals intent on defrauding the system know that
the risk of detection and investigation is fairly low if they stay below
this threshold. So what does the "smart" individual intent on bad
behavior do? They bundle their behaviors differently---and work in
concert with others\-- to stay below the detection threshold. By having
a system in place that detects these networks without false business
rules, the RMA can begin to go after not only the low-hanging fruit, but
the more complex cases with larger aggregate financial payoffs.

I also provide this insight to suggest another fundamental truth in
detecting improper payments. And that is that people engaging in the
type of improper payments change and adapt their behaviors. They develop
new ways of operating to changed environments to stay below the radar.
Consequently, systems must be adopted by RMA that can be refreshed to
stay ahead of these changing behaviors. In short, we are suggesting that
RMA needs a system that is:

-   Agile;

-   Unbiased;

-   Moves away from "pay and chase" models that rely on deterrence and
    collections (which is hard to quantify), to one that is more "trust
    but verify" before payments are made;

-   Based on real-time capabilities.

All of this is possible, with the right resources.

III\. A Holistic Approach to Detecting Improper Payments

I provide this background as a means of setting context, both for my
appearance as well as for the suggestions that Detica has to offer. We
applaud Congress' foresight in the Agriculture Risk Protection Act of
2000 (ARPA) by acknowledging that information technologies are useful
resources to help ensure program integrity. Indeed, ARPA itself has been
instrumental in RMA's ability to augment its efforts to instill
integrity to the crop insurance program. We also acknowledge the role
data mining has played, and the assistance that CAE has provided to RMA
to detect improper payments.

A. Data Mining: Is it just semantics?

At the outset, it is worth pointing out what we believe is a growing
anachronism of ARPA. Clearly, the statute directs the Secretary to use
"data mining" and "data warehousing" to ensure the program integrity of
the crop insurance program. ARPA itself does not define what it means by
data mining. Data mining can mean many things to many people. It can be
used for beneficial purposes, and it can be misused, whether
intentionally or otherwise. Much of the discussion that occurred during
last summer's hearing had nothing to do with data mining, for example,
but was exclusively concerned with RMA's ability to match data with FSA,
which is not data mining at all. Moreover, as we have discussed,
behavior and technologies change, and change rapidly. What might have
been appropriate seven years ago may not be adequate now. While it may
have made sense to direct RMA to use data mining and data warehousing,
without updates to the statute, Congress may actually be locking the
agency into technologies and techniques that are outdated. Consequently,
we would offer that the statute should be revised to provide the
Secretary with greater flexibility in choice of solution and approaches
to stay current with modern day developments. Legislative suggestions
are also attached.

> B. The Importance of Infrastructure

As we have said attempts to find individual anomalies, without more, is
not enough in this changing game of improper payments. As Administrator
Gould noted,

> *A critical area in program integrity improvement is enhancing the
> capability of RMA's IT system. ARPA also instituted new data
> reconciliation, data mining and other anti-fraud, waste and abuse
> activities that require the data to be used in a variety of ways. The
> current IT system was not designed to handle these types of data
> operations. Consequently, the data must be stored in multiple
> databases, which increases data storage costs and processing times,
> and increases the risk of data errors. (May 3, 2007 testimony before
> the House Committee on Oversight and Government Reform).*

These comments were, alarmingly, expanded in RMA's 2008 budget request:

> *As the existing information technology system reaches the end of its
> expected useful life, RMA has experienced increased program down-time
> due to computer outages and increased maintenance costs to keep the
> antiquated system operating. The current system requires RMA to
> maintain multiple databases of producer information magnifying the
> potential for data errors, increases the costs to companies that
> collect and report producer data, and limits the ability of RMA to
> provide participating companies with timely information regarding
> potential program abuse. Additionally, in the nearly 15 years since
> the current system was designed, the Federal Crop Insurance Program
> has increased tremendously in size and scope. New types of insurance
> are being offered which were not contemplated at that time, including
> revenue insurance, whole farm insurance ..., and products tied to
> rainfall and vegetative indexes (i.e. satellite imagery). While these
> programs offer great benefits to producers they also entail a level of
> complexity which is difficult to accommodate in an antiquated computer
> system. Consequently, RMA must use manual work-arounds and off-line
> processes, significantly increasing maintenance and processing costs.*

Congress recognized the department's IT challenges during the debate
surrounding ARPA, and indeed [directed]{.underline} the Secretary to
upgrade the information management systems of the Federal Crop Insurance
Corporation. It also directed the Secretary to make sure that these
upgrades were compatible with other agencies within the Department of
Agriculture.

Based on the Administrator's comments, we assume that the Agency has not
been able to upgrade its internal technological infrastructure, as
envisioned by ARPA. We understand that this is a difficult budgetary
environment, but would submit that the Agency can only be as good as its
weakest link. Right now, RMA's weakest link is that it does not have the
internal infrastructure in place to do basic data matching, much less
advanced analytics. If it does not have the internal information
infrastructure in place to produce, aggregate, and cleanse data, even
the most sophisticated data mining technologies will provide meaningless
results. We would thus urge Congress to make sure that the authorized
and appropriated funding level is adequate to allow RMA to upgrade its
infrastructure and to exercise its oversight authority. By doing so, RMA
will then be in full compliance with all provisions of ARPA, not simply
those sections that permit the use of data mining. And, we urge that
these steps are undertaken and completed before any further thought is
given about expanding the use of analytical tools or information
sharing. To do so would simply be a waste of taxpayer resources because
the infrastructure that it needs to be effective is not in place.

C. RMA's Human Requirements

In our mind, part of that infrastructure has to include having the
workforce internally that understands not only the technologies now and
that might be developed to detect improper payments, but also how to
interpret the results or the outputs from the those technologies. It is
our understanding that all of the data mining expertise resides outside
RMA. This arrangement begs a number of questions: how is it able to
validate the results; how is it able to direct the queries; how can it
assure that it is uncovering the most significant instances of improper
payments, or even a high percentage of the waste; how can it possibly be
a good overseer without its own expertise? Surely, Congress did not
intend for RMA to relinquish its oversight role and authority by
granting the Secretary the discretion to consult with outsiders in
developing the technologies to protect crop insurance integrity.

During the summer of 2006, RMA circulated to the commercial sector a
Request for Information (RFI) that explicitly acknowledges the need to
build its internal capabilities by making training a central requirement
of any contract that results. In pertinent part, the RFI states:

> C.3.5 Training
>
> \(a\) Provide training to RMA personnel on how to utilize the software
> tools developed for the WEB User Interface in this contract.
>
> \(b\) Develop and implement a training plan to train RMA personnel on
> all aspects of developing, maintaining, and enhancing the data
> warehouse. The intent of the training is to provide RMA personnel with
> the capability to assume total operational control of the data
> warehouse at the conclusion of the project with the objective of
> achieving a smooth project transition to RMA.
>
> \(c\) Develop and implement a plan to train RMA personnel on all
> aspects of developing, maintaining, and conducting data mining
> research, data analysis, and pattern recognition using the tools of
> this project. The intent of the training is to provide RMA personnel
> all data mining research, data analysis, and pattern recognition
> operational capabilities at the conclusion of the project.

We strongly support the need to have the internal workforce and would
urge Congress, in its consideration of the Farm Bill reauthorization to
include workforce requirements.

D. Beyond Data Matching and Data Mining

Once RMA addresses its internal infrastructure issues, only then does it
makes sense to discuss data mining and the use of other technologies to
detect fraud.

Our approach envisions creating a "think tank" or "center of excellence"
within RMA. That center would be the principal center within RMA to
oversee crop insurance program integrity. As such, it would have
responsibility to oversee that the infrastructure is in place (including
personnel), and the solutions at its disposal to do a complete job of
oversight. In this regard, it would leverage the data mining that is and
has been done by CAE with more advanced capabilities. These capabilities
include, without limitation, data integration and data cleansing, data
driven investigation, advanced analytics, business process analysis and
results management. To ensure access to current information and
agriculture techniques, we would suggest a continuing consultative role
with CAE, as well as other agricultural and technological experts from
other institutions such as NC State, Virginia Tech, and the University
of Minnesota, to name just a few

Once the center is created, we would suggest that RMA stop thinking
about the analysis of transactions, accounts or even a single view of
the customer, which is what is accomplished by looking for individual
anomalies, but to instead think about what might represent a network of
seemingly loosely related activities. It is only when operating at this
level that the coincidences in the data start producing a bigger
picture. It is this bigger picture that is then risk assessed and it
becomes possible to identify fraud which consists of well spread and
seemingly innocuous activity. It is precisely this current inability to
detect connections that has raised questions by the General
Accountability Office. (*See* Statement of Lisa Shames, Acting Director,
Natural Resources and Environment Division, GAO, May 3, 2007, before the
House Committee on Oversight and Government Reform.)

The key underlying principle is to allow the data to "drive" the
investigation rather than the investigator "mine" the data. Through
automated processing of the data, data- driven investigation uncovers
potentially related rings of questionable behavior and presents these to
the investigator as a complete picture. The clearer picture helps the
investigator, and RMA, better prioritize limited resources. Because
there is more evidence up front to suggest that identified behavior is
questionable, there are fewer false positives---helping the Agency
optimize its investigative resources, with a higher probability of
success.

The key components of a data-driven approach are:

-   [Acquisition and Aggregation]{.underline}: a batch environment that
    collects and aggregates data in a meaningful way to reveal the "big
    picture", and utilizing existing tools and sophisticated techniques
    to overcome issues of seemingly unrelated, poor quality and often
    sparse data. This would include the addition of data from sources
    outside RMA, such as FSA and the insurance companies

-   [Analysis:]{.underline} Use of state-of --the-art techniques that to
    analyze and score cases for potentially bad behavior.

-   [Access:]{.underline} Apply web-based network visualization, search,
    and mapping tools to explore the detected high risk "big" picture.
    This would be a pre-populated environment allowing the investigator
    to immediately explore all the data and relationships without
    additional effort and preparation.

-   [Action]{.underline}: Identifying fraud is not where the effort
    should stop. To reduce fraud, waste, and abuse, the inventory of
    cases needs to be scored and prioritized for investigators to
    maximize the capacity in a manner that enables them to close cases.

What are the benefits to this type of think tank, holistic approach?

-   [More accurate detection]{.underline}. By using multiple data
    sources combined to form a big picture, it is possible to score far
    more accurately. This will result in fewer false positives and time
    wasted on unnecessary investigations. The identification of more
    serious and organized fraud by spanning across accounts, products
    and organizations makes it possible to build pictures of organized
    crime networks. This helps to overcome problems the RMA currently
    has integrating the information, particularly on large and disparate
    farming entities.

-   [More efficient investigation]{.underline}. Generally, there are
    more leads than investigators have time to pursue. Through an
    approach that includes pre-aggregating data, RMA investigators have
    a complete network diagram of all elements of the crime. This saves
    time in not having to collect intelligence and piece the picture
    together by hand---enabling the investigator to process more leads,
    and more worthwhile leads.

-   [Better intelligence]{.underline}. By centrally retaining data,
    fraud rings and investigations have enhanced access to intelligence,
    and intelligence that they can comprehend. By understanding the
    modus operandi of those involved with improper payments, better
    measures can be implemented to prevent the crime in the first
    instance.

-   [More targeted investigations and use of responses.]{.underline}
    Data-driven investigations will reveal networks and connections,
    particularly useful to reveal new and emerging patterns much more
    rapidly than other types of tools. If behaviors are allowed to
    become entrenched, they become much harder to identify and address.
    More important, the cumulative losses are significant if detection
    is delayed until there is a critical mass of behaviors to uncover.
    Agile, easily refreshed approaches that use advanced analytics and
    network detection enable early detection and response, and fewer
    losses to the Agency.

-   [Prevention]{.underline}. With its ability to retain the outcomes of
    investigations, as well as more accurate detection, it is possible
    to link the "solution" directly into key stages in the business
    process. For example, by checking new applicants or significant
    transactions against the intelligence database, escalating crime can
    be halted. This will help RMA with its oversight of the insurance
    companies.

A holistic, linked approach is not simply good for RMA. The Department
of Agriculture (USDA) benefits through maximizing the use of data across
all departments---which is a central theme of ARPA. Besides efficiencies
in data management, it will help USDA create efficiencies in the
application of data. Farmers benefit if RMA has better information.
Reducing dollars lost to fraud offers the potential for reduced
insurance premiums and/or the expansion of programs to those truly in
need. Insurance companies will experience more efficient oversight and
control, reducing the management burden of the program. Finally,
taxpayers benefit through having their resources effectively managed,
and managed as intended.

Again, I thank for this opportunity to appear before you today and am
happy to answer any questions you may have.

LEGISLATIVE RECOMMENDATIONS:

1.  Update ARPA to permit the Secretary greater flexibility in the
    technological choices employed. This could be accomplished by simply
    striking 7 U.S.C. 515(j)(2) and rewriting it to read as follows:

> *The Secretary shall use such information technology as he deems
> appropriate to administer the provisions of this title. Such
> technologies may include, without limitation, predictive analytics,
> modeling, pattern and network detection.*

2.  Require the Secretary to report back to Congress, within 45 days of
    enactment, as to the existing state of technologies that it now
    uses, what its requirements are, and the funding that it will take
    to fully modernize. Condition the expansion of information sharing
    to certain metrics related to upgrading the internal information
    technology infrastructure.

3.  Set internal workforce requirements necessary to maintain and update
    the information systems and analytics necessary for a thorough,
    comprehensive risk management system.

4.  Create an internal center within RMA tasked exclusively with fraud
    detection. This center should have responsibility to oversee the
    technology upgrades that must occur, vet and deploy technological
    solutions, attract, recruit and retain qualified internal personnel,
    and consult with outside consultants as necessary. Additionally, the
    range of outside consultants should be expanded to include not only
    CAE, but any other academic institution, including agricultural
    cooperatives, agricultural universities, and extension programs.

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