list can lead to substantial recoverable
amounts and/or cash savings.
Especially if a number of operating
companies have been put into
bankruptcy, a review of vendors
across the companies may lead to
consolidations and reveal balances
in favor of the bankrupt company.
In some instances these balances in
favor of the company take the form
of cash; in others, they may involve
other types of consideration (e.g.,
advertising credits in a retail company).
Consolidating vendors, either within
a vendor master list or across related
companies, usually depends on
a process called entity resolution.
Entity resolution involves analyzing
company names and available
identifying information to determine
whether a company appears multiple
times under different names. In one
case involving more than 50 debtor
companies, vendor list cleanup
produced an aggregate balance in the
payables ledger of more than $1 billion
in favor of the combined company.
Similarly, vendor list cleanup can
enable the company to seek global
volume discounts. To the extent that a
vendor is listed multiple times and is
billing for similar items, it is possible
that contractual volume discounts,
available for one of the companies,
are not being realized for others, or
that true purchase volumes across
the vendors are not guiding prices.
Combining vendor instances through
an entity resolution process may reveal
unrealized discounts that can either
be recovered or sought in the future.
Keys to Fraud Analysis
In addition to analyses aimed at
finding particular schemes, effective
fraud, waste, and misuse analysis
for recovery purposes should have
multiple dimensions, from the scope
of the enterprise under review to the
techniques and tools employed.
For example, many organizations take a
channel, or silo, approach to detecting
fraud, with a risk or event in one channel
not necessarily considered in another
part of the organization. During a
review of a bankrupt company’s books
and records, a more holistic approach
to the recovery analysis may reveal
previously undetected fraud risks
and patterns across business units,
channels, departments, and functions.
Temporal scope is another important
dimension. In bankruptcy proceedings
that stretch over many months or
even years, effective recovery analysis
might include the ability to screen
for fraudulent behavior or indicators
on a real-time basis. This may allow
a company to identify and remedy a
fraud issue before it fully manifests itself
and causes damage to a business that
is trying to emerge from Chapter 11.
In a recent trend, many data scientists
are moving from the laboratory into the
operational environment. This means
that advanced techniques, such as
predictive analytics, social networking
analysis, and geospatial analysis that
have been used experimentally in labs,
are migrating into the operational sphere,
providing recovery specialists new ways
to identify fraud, waste, and misuse,
and the related recoverable value.
At the same time, the very nature of data
has rapidly evolved. Spreadsheets and
structured accounting data are giving
way to complex, unstructured data sets
across domains, such as email, social
media, texts, and video. The advent
of big data—terabytes, petabytes, and
beyond—introduces further complexity.
Going forward, a comprehensive
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Learn more about AloStar Thinking Capital at businesscredit.alostarbank.com
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