An Analysis of Data Mining Applications for Fraud Detection in Securities Market

Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume: 03 Issue: 01 June 2014, Page No. 9- 15
ISSN: 2278-2419
9
An Analysis of Data Mining Applications for
Fraud Detection in Securities Market
S.Dhanalakshmi1
, C.Subramanian2
1
Research Scholar, Department of Computer Applications, Dr. M.G.R. Educational and Research Institute University, Chennai
2
Assistant professor, Department of Computer Applications, Dr. M.G.R. Educational and Research Institute University, Chennai
Email: dhanu212@gmail.com , cspratha@gmail.com
Abstract-In recent securities fraud broadly refers to deceptive
practices in connection with the offering for sale of securities.
There are many challenges involved in developing data mining
applications for fraud detection in securities market, including:
massive datasets, accuracy, privacy, performance measures and
complexity. The impacts on the market and the training of
regulators are other issues that need to be addressed. In this
paper we present the results of a Comprehensive systematic
literature review on data mining techniques for detecting
fraudulent activities and market manipulation in securities
market. We identify the best practices that are based on data
mining methods for detecting known fraudulent patterns and
discovering new predatory strategies. Furthermore, we
highlight the challenges faced in the development and
implementation of data mining systems for detecting market
manipulation in securities market and we provide
recommendation for future research works accordingly.
I. INTRODUCTION
Securities fraud, also known as stock fraud and investment
fraud, is a deceptive practice in the stock or commodities
markets that induces investors to make purchase or sale
decisions on the basis of false information, frequently resulting
in losses, in violation of securities laws.[1]
Offers of risky
investment opportunities to unsophisticated investors who are
unable to evaluate risk adequately and cannot afford loss of
capital is a central problem. Securities fraud can also include
outright theft from investors
(embezzlement by stockbrokers), stock manipulation,
misstatements on a public company's financial reports, and
lying to corporate auditors. The term encompasses a wide
range of other actions, including insider trading, front
running and other illegal acts on the trading floor of a stock or
commodity exchange.
II. REVIEW OF LITERATURE
A. Canadian Securities Administrators (CSA) 2010 report
The Canadian Securities Administrators (CSA or we) are
examining the mutual fund fee structure in Canada in order to
see whether there are investor protection or fairness issues, and
to determine whether any regulatory responses are needed to
address any issues we find. This paper is intended to be a
platform to begin a discussion on the current mutual fund fee
structure in Canada. This discussion paper is the first step in
the CSA’s public consultations about this project. It
• provides an overview of the roles of the market participants
in the mutual fund industry (mutual fund manufacturers and
advisors who distribute the funds)
• provides an overview of the current mutual fund fee structure
• identifies some investor protection and fairness issues we
think arise from the current fee structure
• provides an overview of global regulatory reforms
• describes some regulatory options the CSA could potentially
consider, either alone or in combination. Some of the options
would impact mutual funds or mutual fund manufacturers
directly, and others would impact those who sell the product.
III. CANADIAN MUTUAL FUND INDUSTRY
PARTICIPANTS
The participants in the Canadian mutual fund industry include
the mutual fund manufacturers who produce and promote
mutual fund products and advisors who distribute those
products to investors.
1. The mutual fund manufacturers
There are currently 103 mutual fund manufacturers in Canada.
They fall into the following four categories:
i. Canadian banks/deposit-takers
The fund management arms of 7 Canadian chartered banks
together with the Movement Desjardins in Québec currently
account for 43% of mutual fund assets under management.
These manufacturers largely distribute their mutual funds
through their branch networks, full-service and discount
brokerage networks. Most of them also distribute a separate
series of securities of their mutual funds, known as the Advisor
series, through third party advisors.
ii. Life insurers
While Canadian life insurance companies primarily produce
and promote segregated fund products, they are also involved
in manufacturing mutual funds. These manufacturers currently
represent 4.6% of mutual fund assets under management.
iii. Independents
Independent mutual fund manufacturers are those that are not a
subsidiary of one of the large deposit-taker institutions. These
independents manage the largest share of industry assets and
currently represent 49.4% of mutual fund assets under
management.

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iv. Unions and Associations
The remaining 3% of mutual fund industry assets are managed
by unions and associations. Mutual funds produced and
promoted by these manufacturers are generally organized for
specific target groups (e.g. teachers, physicians) and generally
only members of those groups can buy them.
2. Current mutual fund fees
a. Sales charges
Most Canadian mutual fund manufacturers sell funds under
several different purchase options. The options relate generally
to the method in which the sales charges are paid. The mutual
fund manufacturers set the rate of sales charges that may be
payable under the various purchase options.
The different purchase options are:
 Front-end sales charge
 Under this option, investors pay a sales commission directly
to the advisor at the time they buy securities of the mutual
fund.
 This is often referred to as a “front-end load”. The advisor’s
sales commission is deducted from the total amount paid by the
investor, which means only the remaining amount is invested
in the fund.
 While the sales commission set by the mutual fund
manufacturer may the purchase amount, investors. typically
negotiate a lower sales commission with their advisor. Over the
last few years, we understand that Canadian advisors
3. Deferred sales charge (DSC)
 Under this option, investors pay a sales charge at the time
they redeem from the mutual fund, rather than at the time of
purchase. This is often called a “back-end load”. This allows
the entire amount paid by the investor to be invested in the
mutual fund at the time of purchase.
 The rate of the DSC payable by investors when they redeem
declines the longer they hold the investment and becomes nil
after a specified holding period. This is known as the
“redemption schedule”. The DSC paid by an investor is
typically around 6% in Depending on the mutual fund
manufacturer’s DSC policy, the amount of the DSC an investor
pays on a redemption can be based either on the original
purchase price of the mutual fund securities or their current
market value when they are redeemed.
 Investors can avoid DSCs by holding their mutual
investment until the end of the redemption schedule or
redeeming no more to another within the same fund family
without a charge.
 While the investor does not directly pay a sales commission
to the advisor at the time of purchase, the advisor typically
receives a commission from the mutual fund manufacturer
equivalent to 5% of the amount purchased. The mutual fund
manufacturer will generally borrow the money necessary to
pay these advisor commissions and therefore will incur
financing costs. These costs are recouped by the mutual fund
manufacturer through ongoing management fees charged to the
fund
 DSCs paid by investors who redeem before the end of the
redemption schedule are not paid to the advisor or the mutual
fund, but rather to the mutual fund manufacturer or third party
financing services provider that paid the advisor’s sales
commission at the time of purchase.
4. Low-load sales charge
Many mutual fund manufacturers offer a low-load sales charge
option, which works like the DSC option described above, but
on a shorter redemption schedule. The commission paid by a
mutual fund manufacturer to the advisor at the time the
investor purchases securities of a fund on a low-load basis
typically purchase amount. Funds sold on a no-load basis do
not offer any sales commission to advisors (either one paid by
the investor or the mutual fund manufacturer), nor do they
charge a fee at the time the investor redeems Mutual funds
purchased on a no-load basis in Canada are generally bought
directly from the mutual fund manufacturer or an affiliate,
either of which must be a registered dealer firm.The FBI
focuses its financial crimes investigations on such criminal
activities as corporate fraud, securities and commodities fraud,
health care fraud, financial institution fraud, mortgage fraud,
insurance fraud, mass marketing fraud, and money laundering
.These are the identified priority crime problem areas of the
Financial Crimes Section (FCS) of the FBI.Mission: The
mission of the FCS is to oversee the investigation of financial
fraud and to facilitate the forfeiture of assets from those
engaging in federal crimes .
5. White Collar Crime (WCC) National Priorities
Based upon FBI field office threat strategies and directives
established by the President, the Attorney General, the
Director, and the Criminal Investigative Division (CID), the
following national priorities for the WCC Program (WCCP)
have been established: public corruption, corporate
fraud/securities fraud to include Ponzi schemes, health care
fraud, FIF (to include bank failures and mortgage
fraud),insurance fraud, money laundering, and mass marketing
fraud .Although public corruption is a national priority within
the WCCP.
IV. CORPORATE FRAUD
As the lead agency investigating corporate fraud, the FBI has
focused its effort son cases which involve accounting schemes,
self-dealing by corporate executives, and obstruction of justice.
The majority of corporate fraud cases pursued by the FBI
involve accounting schemes designed to deceive investors,
auditors, and analysts about the true financial condition of a
corporation or business entity.
V. SECURITIES AND COMMODITIES FRAUD
The continued uncertainty and volatility of today’s financial
markets could be measured by the Dow Jones Industrial
Average movement from 12,681 on July 22, 2011, to10, 655
on October 3, 2011. As a result of such tumultuous markets,

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the FBI witnessed a steady rise in securities and commodities
frauds as investors sought alternative investment opportunities.
Investment Fraud: These schemes, sometimes referred to as
High YieldInvestment Fraud, involve the illegal sale or
purported sale of financial instruments. Financial instruments
are defined broadly as any contract that gives rise to a financial
asset of one entity and a financial liability or equity instrument
to another entity.
The following are definitions of the most common investment
fraud scheme variations:
Ponzi Schemes - A Ponzi scheme is an investment fraud that
involves thepayment of purported returns to existing investors
from funds contributed by new investors .Ponzi schemes often
share common characteristics such as offering overly
consistent returns ,unregistered investments, high returns with
little or no risk, or secretive or complex strategies.
Affinity Frauds - Perpetrators of affinity frauds take advantage
of the ten of people to trust others with whom they share
similarities such as religion or ethnic identity to gain their trust
and money.
Pyramid Schemes - In pyramid schemes, as in Ponzi schemes,
money collected from new participants is paid to earlier
participants. In pyramid schemes, however, participants
receive commissions for recruiting new participants into the
scheme.
A. Alberta Securities Commission (ASC) 2010 report
The Alberta Securities Commission (ASC) is pleased to
publish its 23rd annual Corporate Finance Disclosure Report
(Report). The Report continues to be an important vehicle to
share with market participants our observations on public
disclosure provided by Alberta reporting issuers (RIs).
B. Liquidity and Capital Resources
In last year’s report we noted several areas relating to liquidity
and capital resources where RI disclosures could be improved.
These areas included financial statement, MD&A and
prospectus disclosures regarding financial covenants, risks, and
working capital.
C. Overview of the Research work
Analysis the securitites Market using Data Mining:
Data mining is the process of discovering patterns in large data
sets involving methods at the database systems. The overall
goal of the data mining process is to extract information from a
data set and transform it into an understandable structure for
further use. Aside from the raw analysis step, it involves
database and data management aspects, data
preprocessing, model and inference considerations,
interestingness metrics, complexity considerations, post-
processing of discovered structures, visualization, and online
updating.
D. Relational Databases
A relational database is a collection of tables, each of which is
assigned a unique name. Each table consists of a set of
attributes (columns or fields) and usually stores a large set of
tuples (records or rows). Each tuple in a relational table
represents an object identified by a unique key and described
by a set of attribute values.
Data mining involves six common classes of tasks:
 Anomalydetection(Outlier/change/deviation detection) – The
identification of unusual data records, that might be interesting
or data errors that require further investigation.
 Association rule learning (Dependency modeling) –
Searches for relationships between variables. For example a
supermarket might gather data on customer purchasing habits.
Using association rule learning, the supermarket can determine
which products are frequently bought together and use this
information for marketing purposes. This is sometimes referred
to as market basket analysis.
 Clustering – is the task of discovering groups and structures
in the data that are in some way or another "similar", without
using known structures in the data.
 Classification – is the task of generalizing known structure to
apply to new data. For example, an e-mail program might
attempt to classify an e-mail as "legitimate" or as "spam".
 Regression – attempts to find a function which models the
data with the least error.
 Summarization – providing a more compact representation
of the data set, including visualization and report generation.
E. Object-Relational Databases
Object-relational databases are constructed based on an object-
relational data model. This model extends the relational model
by providing a rich data type for handling complex objects and
object orientation.
These patterns and trends can be collected and defined as
a data mining model. Mining models can be applied to specific
scenarios, such as:
 Forecasting: Estimating sales, predicting server loads or
server downtime
 Risk and probability: Choosing the best customers for
targeted mailings, determining the probable break-even point
for risk scenarios, assigning probabilities to diagnoses or other
outcomes
 Recommendations: Determining which products are likely to
be sold together, generating recommendations
 Finding sequences: Analyzing customer selections in a
shopping cart, predicting next likely events
 Grouping: Separating customers or events into cluster of
related items, analyzing and predicting affinities
Temporal Databases, Sequence Databases, Time-Series
Databases and Spatial Databases
 A temporal database typically stores relational data that
include time-related attributes. These attributes may involve
several timestamps, each having different semantics.
 A sequence database stores sequences of ordered events,
with or without a concrete notion of time. Examples include

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customer shopping sequences, Web click streams, and
biological sequences.
 A time-series database stores sequences of values or events
obtained over repeated measurements of time (e.g., hourly,
daily, weekly). Examples include data collected from the stock
exchange, inventory control, and the observation of natural
phenomena (like temperature and wind).
 Spatial databases contain spatial-related information.
Examples include geographic (map) databases, very large-scale
integration (VLSI) or computed-aided design databases, and
medical and satellite image databases.
Data mining works
While large-scale information technology has been evolving
separate transaction and analytical systems, data mining
provides the link between the two. Generally, any of four types
of relationships are sought:
 Classes: Stored data is used to locate data in predetermined
groups. For example, a restaurant chain could mine customer
purchase data to determine when customers visit and what they
typically order. This information could be used to increase
traffic by having daily specials.
 Clusters: Data items are grouped according to logical
relationships or consumer preferences. For example, data can
be mined to identify market segments or consumer affinities.
 Associations: Data can be mined to identify associations.
The beer-diaper example is an example of associative mining.
 Sequential patterns: Data is mined to anticipate behavior
patterns and trends. For example, an outdoor equipment retailer
could predict the likelihood of a backpack being purchased
based on a consumer's purchase of sleeping bags and hiking
shoes.
F. Data mining consists of five major elements:
 Extract, transform, and load transaction data onto the data
warehouse system.
 Store and manage the data in a multidimensional database
system.
 Provide data access to business analysts and information
technology professionals.
 Analyze the data by application software.
 Present the data in a useful format, such as a graph or table.
Different levels of analysis are available:
 Artificial neural networks: Non-linear predictive models that
learn through training and resemble biological neural networks
in structure.
 Genetic algorithms: Optimization techniques that use
processes such as genetic combination, mutation, and natural
selection in a design based on the concepts of natural
evolution.
 Decision trees: Tree-shaped structures that represent sets of
decisions. These decisions generate rules for the classification
of a dataset. Specific decision tree methods include
Classification and Regression Trees (CART) and Chi Square
Automatic Interaction Detection (CHAID) . CART and
CHAID are decision tree techniques used for classification of a
dataset. They provide a set of rules that you can apply to a new
(unclassified) dataset to predict which records will have a
given outcome. CART segments a dataset by creating 2-way
splits while CHAID segments using chi square tests to create
multi-way splits. CART typically requires less data preparation
than CHAID.
 Nearest neighbor method: A technique that classifies each
record in a dataset based on a combination of the classes of
the k record(s) most similar to it in a historical dataset
(where k 1). Sometimes called the k-nearest neighbor
technique.
 Rule induction: The extraction of useful if-then rules from
data based on statistical significance.
 Data visualization: The visual interpretation of complex
relationships in multidimensional data. Graphics tools are used
to illustrate data relationships.
G. Cluster analysis
Cluster analysis or clustering is the task of grouping a set of
objects in such a way that objects in the same group (called
a cluster) are more similar (in some sense or another) to each
other than to those in other groups (clusters).
a. Centroid based clustering
In centroid-based clustering, clusters are represented by a
central vector, which may not necessarily be a member of the
data set.When the number of clusters is fixed to k, k-means
clustering gives a formal definition as an optimization
problem: find the cluster centers and assign the objects to
the nearest cluster center, such that the squared distances from
the cluster are minimized.
b. Connectivity based clustering (hierarchical clustering)
Connectivity based clustering, also known as hierarchical
clustering, is based on the core idea of objects being more
related to nearby objects than to objects farther away. These
algorithms connect "objects" to form "clusters" based on their
distance.
c. Distribution-based clustering
The clustering model most closely related to statistics is based
on distribution models. Clusters can then easily be defined as
objects belonging most likely to the same distribution.
Distribution-based clustering is a semantically strong method,
as it not only provides you with clusters, but also produces
complex models for the clusters that can also capture
correlation and dependence of attributes.
d. Density-based clustering
In density-based clustering, clusters are defined as areas of
higher density than the remainder of the data set. Objects in
these sparse areas - that are required to separate clusters - are
usually considered to be noise and border points.
e. Algorithms for mining data streams
Frequent Pattern Mining
(1) Mining Multiple Datasets
In many situations, such as in a data warehouse, the user
usually has a view of multiple datasets collected from different
data sources or from different time points.
(2) Mining Data Streams

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In recent years, database and data mining communities focus
on a new model of data processing, where data arrives in the
form of continuous streams. Because it is not feasible to store
all data, it is quite challenging to perform the traditional data
mining operations in a streaming environment. Our current and
proposed research focuses on many of the challenges
associated with mining streaming data. Particularly, we are
driven by 1) the need to have low memory requirements, as
stream mining is often carried out in small and hand held
devices that do not have much memory, 2) some variants of the
mining that may be desirable in a streaming environment, and
3) the need for having high accuracy and confidence in the
results generated.
(3) New Data Structures for Maintaining Frequent
Itemsets When mining frequent itemsets on data streams, or
caching the results of mining operators in CMT queries, there
are several common operations.
Efficient and Exact K-Means Clustering on Very Large
Datasets-K-means requires several passes on the entire dataset,
which can make it very expensive for large disk-resident
datasets. In view of this, a lot of work has been done on
various approximate versions of k-means, which require only
one or a small number of passes on the entire dataset.Efficient
and Effective Decision Tree Construction on Streaming Data
Decision tree construction is a well studied problem in data
mining. Recently, there has been much interest in mining
streaming data. Domingos and Hulten have proposed a one-
pass algorithm for decision tree construction. Their work uses
Hoeffding inequality to achieve a probabilistic bound on the
accuracy of the tree constructed.
VI. METHODOLOGY
Architecture Diagram
Figure-1
Online transaction processing or OLTP is a class
of information systems that facilitate and manage transaction-
oriented applications, typically for data entry and
retrieval transaction processing.
Customer can buy the shares through the Multiple Trusted
Agents. The buying details is stored in separate databases.
Multiple Agents is sending the datas to the Admin Agent. In
between time the Multiple Agents can misuse the amounts. i.e.,
Agent invest in fake account. Then the fake investment is
stored in separate databases. So Admin agent can check
original collected proof which is sent by customers and the
fake investment details.Then fake investment details are shown
in admin agent.
A. Trusted agent Detector
Detector is the assignment of a label to a given input value. An
example of pattern recognition misclassification, which
attempts to assign each input value to one of a given set
of classes (for example, determine whether a given email is
"spam" or "non-spam"). However, pattern recognition is a
more general problem that encompasses other types of output
as well.
Identify Fake AgentAgent administrator will veify all the data
which is going out from the network and compare with original
dataset and if there is any mismatch in the data with original
dataset then it is considered as fake transcation and it will
easily track the fake agent from the multiple agent groups.
Finally, the fake agent will be marked in black list.
we annotated papers based on the data mining approach (i.e.
supervised, unsupervised and semisupervised learning), data
mining method (eg. classification, outlier detection, social
network analysis), data mining challenges, and issues.
VII. RESULT
A. High Frequency Trading
High Frequency Trading (HFT) is an evolution of securities
market that adopts sophisticated algorithms to automaticallfay
analyze and react to market data in milliseconds. It is estimated
that HFT accounts for 35% of the stock market trades in
Canada and 70% of the stock trades in USA [18].
HFT strategies can be divided into five categories:
1) Passive Rebate Arbitrage: providing liquidity and receiving
incentives from exchanges similar to market makers that is one
of the major HFT strategies
2) Latency Arbitrage: making profit through buying and selling
the same security between domiciles (i.e. Inter-listed
Arbitrage) or between domestic marketplaces (i.e. Intra-listed
Arbitrage)
3) Information Arbitrage: making profit through buying and
selling the same security at a higher price by taking advantage
of “mispricing between the various forms of a tradable index“
4) Statistical Arbitrage: applying statistical methods to detect
trading patterns for relative value trading
5) Market Structure Trading: making profit through trading
opportunities that are a consequence of the new market
structure such as flash orders, locked markets, and dark pools
While HFT supports liquidity and contributes to price
formation in the market, it might have negative impacts in
adverse market conditions. Regulators have been considering
trading obligations and supervision on HFT especially after the
May 6 flash crash. Both growth and impact of HFT in stock
market have brought great interests of industry and thus

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requires regulators to establish an environment to support fair
and orderly trading market. Unlike traditional trading, HFT is
not subject to significant trading obligations and there is very
little public information regarding fraudulent patterns and
activities of HFT systems. Data mining techniques can be
employed to identify fraudulent activities and predatory
strategies in HFT.
B. Massive Data
The datasets in securities market are huge. There are over 2700
securities listed in NAZDAQ and Super Montage (NASDAQ’s
trading platform) facilitates more than 5000 transactions per
second. Similarly, the number of transactions show a
significant growth from 250,000 to 5 million orders per day a
few years ago, to 700,000 to 40 million orders each day3. The
rate of growth in the amount of data is rapidly increasing due
to changes in trading strategies by both buy-side and sell-side
firms. Thus, there is extra pressure on industry to
accommodate faster trading systems and on regulatory
organizations to adapt to new strategies.
Figure-2
Figure-3
Figure-4
Figure-5
Figure-6
Company name is given by customer for searching the share
details. Then it will show all details of shares like date, trade,
volume, high, low and chart.If customer needs to buy the share
of company then click the Buy button. Then customer can buy
the number of shares.When the customer buy the share, that
details stored in Agent side. Afterthat the agent can change the
amount as duplicate before sending to the company. when the
server is started, the share details stored in database.When the
company side is report is generated, the company can detect
the fraud in share values which is marked by red and green
colour. The Fraud shares is filled by red. Green colour
represented the non fraud share values.
VIII. CONCLUSION
The significant growth of the capital market due to increasing
interests in investing in securities market requires regulatory
organizations to expand their efforts to ensure a fair and
orderly market for the participants. Data mining methods are
effective in detecting different types of fraud in securities
market. In this paper we reviewed the most significant data
mining methods that are applied to monitoring securities
market and detecting market manipulation. We highlighted
numerous challenges that are involved in developing data
mining methods for detection and prevention of fraudulent
activities in securities market. Some of the challenges in
designing and developing data mining methods include
massive datasets, different sources and forms of data and using
appropriate performance measures to evaluate the method. We
also provided directions for new research in this field.
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