Tag Archives: relationship detection

Crime Tech Solutions Acquires Case Closed Software

June 1, 2016 (Austin, TX)   Crime Tech Solutions, LLC, a leading provider of analytics and investigation software for law enforcement and commercial markets, today announced that it has acquired Cleveland, TN based Case Closed Software in a cash transaction. The terms of the deal were not released, but according to Crime Tech Solutions’ founder and president Douglas Wood, the acquisition brings together two dynamic and fast-growing software companies with an unparalleled complement of technologies.
For Crime Tech Solutions, the opportunity to add Case Closed Software into the fold was too good to pass up” said Mr. Wood. “We think that the technology offered by Case Closed helps to further differentiate us in the market as the price performance leader for this type of investigative solution.PNG

Crime Tech Solutions, based in the city of Leander, TX, delivers advanced analytics and investigation software to commercial investigators and law enforcement agencies across the globe. Their solution suite includes criminal intelligence software, sophisticated crime analytics with geospatial mapping, and powerful link analysis and visualization software. The company says that the addition of Case Closed Software expands those offerings even further.

CaseClosed1Case Closed Software develops and markets investigative case management software specifically designed for law enforcement agencies. The suite is built around four primary software products including best-in-class investigative case management software, property and evidence tracking, a gang database tool, and an integrated link analysis and data visualization tool.

Case Closed couldn’t be happier than to be joining Crime Tech Solutions,” said Keith Weigand, the company’s founder. “The blending of our technologies creates a suite that will add tremendous value to our mutual customers, and will be hard for others to duplicate.

According to both Mr. Weigand and Mr. Wood, the name Case Closed will continue on as the product brand, given its widespread popularity and loyal customer base. Crime Tech Solutions is expected to retain all Case Closed employees, with Mr. Weigand joining as the company’s chief technical officer.

Crime Tech Solutions says it expects continued growth via ongoing software sales and strategic acquisitions.

About Crime Tech Solutions

(NOTE: Crime Tech Solutions is an Austin, TX based provider of crime and fraud analytics software for commercial and law enforcement groups. Our offerings include sophisticated Case Closed™ investigative case management and major case management, GangBuster™ gang intelligence software, powerful link analysis software, evidence management, mobile applications for law enforcement, comprehensive crime analytics with mapping and predictive policing, and 28 CFR Part 23 compliant criminal intelligence database management systems.)

Link Analysis and Crime – An examination.

Posted by Tyler Wood, Operations Manager at Crime Tech Solutions

Pic003The topic of fraud is widely discussed, and the focus of thousands upon thousands of articles. Television shows such as Crime, Inc and American Greed have become popular due, in part, to our fascination with the topic of fraud.

The organizations that are affected by fraud are also fascinated… but for entirely different reasons. Some estimates suggest that the US economy loses 11 trillion dollars each year due to one form of fraud or another. It’s little wonder, therefore, that the companies most frequently defrauded have been heavily investing in anti-fraud technologies at an increasing rate over the past decade or more.

The biggest problem with fraud, of course, is that it is always evolving in a very Darwinian fashion. Like a living, breathing entity, fraud schemes change over time in order to survive. As the targets of fraud schemes put new policies, procedures and/or systems to deter the activities, the schemes modify and find new ways to survive.

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So, since the nature of criminal activity is such that they constantly change, how do investigators find a fool proof methodology to ensure they are 100% safe from them? The answer, of course, is that they can’t. They never will; at least not until we live in a world such as the one depicted in the 2002 film Minority Report, starring Tom Cruise. In that movie, criminals are arrested prior to committing a crime based upon the predictions of psychics called ‘Precogs’. Corporations and individual targets of fraud can only wish.

Nope, there are no Precogs running around locking up would-be practitioners of fraud that would protect banks, insurance companies, Medicaid and Medicare programs, victims of Ponzi schemes, victims of identity theft, and countless others. Instead, organizations rely upon skilled knowledge workers using purpose-built crime and fraud analytics technology that can detect anomalies in patterns, suspicious transactions, hotspot mapping, networks of fraudsters, and other sophisticated data analytics tools.

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Crime and fraud analytics

Any discussion of analytics and investigation software must touch upon the topic of ‘big data’. No longer just a buzz word, big data literally fuels the insights gathered by organizations in every area of business. Naturally, then, organizations who have been traditionally targeted by fraudsters have increasingly invested in crime technology such as investigation software and analytics in order to exploit the phenomenon.

gotbigdata.pngOf course, big data in and by itself does nothing. It just sits there. Nobody has ever yelled “Help! We’ve been defrauded! Call the big data!” Big data is only useful when it can be transformed into ‘smart data’. In other words, understanding the big picture of costly fraudulent activities is not akin to understanding the specifics of ‘who’ is defrauding you, and ‘how’ they are doing it.

Those questions can best be answered through the powerful data mining and link analysis software tools offered by Austin, TX based Crime Tech Solutions in partnership with Sterling, VA based Visallo. Effective link analysis complements big data analytics platforms, helping to expose previously undetected fraud, and the entities (people or organizations) committing it.

Link Analysis – Transforming big data into smart data

By definition, link analysis is a data analysis technique that examines relationships among people, places, and things. As a visual tool, link analysis provides users a powerful method to quickly understand and ‘see’ what is happening. Because of this, it is widely used by financial institutions such as banks and insurance companies to uncover criminal networks, improve fraud investigations, detect insider fraud, and expose money laundering schemes. Similarly, government agencies use link analysis to investigate fraud, enhance screening processes, uncover terrorist networks and investigate criminal activities.

At Crime Tech Solutions, we liken the question of how to detect and deter fraud to ‘How do you eat an elephant?’ The answer, of course, is one bite at a time. If big data is the elephant, comprehensive link analysis software is part of the one ‘bite’ at a time. Or should we say ‘byte’.

(NOTE: Crime Tech Solutions is an Austin, TX based provider of investigation software and analytics for commercial and law enforcement groups. We proudly support the Association of Certified Fraud Examiners (ACFE), International Association of Chiefs of Police (IACP), Association of Law Enforcement Intelligence Units (LEIU) and International Association of Crime Analysts (IACA). Our offerings include sophisticated link analysis software, an industry-leading investigation case management solution, and criminal intelligence database management systems.)

What is Link / Social Network Analysis?

Posted by Crime Tech SolutionsPic003

Computer-based link analysis is a set of techniques for exploring associations among large numbers of objects of different types. These methods have proven crucial in assisting human investigators in comprehending complex webs of evidence and drawing conclusions that are not apparent from any single piece of information. These methods are equally useful for creating variables that can be combined with structured data sources to improve automated decision-making processes. Typically, linkage data is modeled as a graph, with nodes representing entities of interest and links representing relationships or transactions. Links and nodes may have attributes specific to the domain. For example, link attributes might indicate the certainty or strength of a relationship, the dollar value of a transaction, or the probability of an infection.

Some linkage data, such as telephone call detail records, may be simple but voluminous, with uniform node and link types and a great deal of regularity. Other data, such as law enforcement data, may be extremely rich and varied, though sparse, with elements possessing many attributes and confidence values that may change over time.

Various techniques are appropriate for distinct problems. For example, heuristic, localized methods might be appropriate for matching known patterns to a network of financial transactions in a criminal investigation. Efficient global search strategies, on the other hand, might be best for finding centrality or severability in a telephone network.

Link analysis can be broken down into two components—link generation, and utilization of the resulting linkage graph.

Link Generation

Link generation is the process of computing the links, link attributes and node attributes. There are several different ways to define links. The different approaches yield very different linkage graphs. A key aspect in defining a link analysis is deciding which representation to use.

Explicit Links

A link may be created between the nodes corresponding to each pair of entities in a transaction. For example, with a call detail record, a link is created between the originating telephone number and the destination telephone number. This is referred to as an explicit link.

Aggregate Links

A single link may be created from multiple transactions. For example, a single link could represent all telephone calls between two parties, and a link attribute might be the number of calls represented. Thus, several explicit links may be collapsed into a single aggregate link.

Inferred Relationships

Links may also be created between pairs of nodes based on inferred strengths of relationships between them. These are sometimes referred to as soft links, association links, or co-occurrence links. Classes of algorithms for these computations include association rules, Bayesian belief networks and context vectors. For example, a link may be created between any pair of nodes whose context vectors lie within a certain radius of one another. Typically, one attribute of such a link is the strength of the relationship it represents. Time is a key feature that offers an opportunity to uncover linkages that might be missed by more typical data analysis approaches. For example, suppose a temporal analysis of wire transfer records indicates that a transfer from account A to person X at one bank is temporally proximate to a transfer from account B to person Y at another bank. This yields an inferred link between accounts A and B. If other aspects of the accounts or transactions are also suspicious, they may be flagged for additional scrutiny for possible money laundering activity.

A specific instance of inferred relationships is identifying two nodes that may actually correspond to the same physical entity, such as a person or an account. Link analysis includes mechanisms for collapsing these to a single node. Typically, the analyst creates rules or selects parameters specifying in which instances to merge nodes in this fashion.

Utilization

Once a linkage graph, including the link and node attributes, has been defined, it can be browsed, searched or used to create variables as inputs to a decision system.

Visualization

In visualizing linking graphs, each node is represented as an icon, and each link is represented as a line or an arrow between two nodes. The node and link attributes may be displayed next to the items or accessed via mouse actions. Different icon types represent different entity types. Similarly, link attributes determine the link representation (line strength, line color, arrowhead, etc.).

Standard graphs include spoke and wheel, peacock, group, hierarchy and mesh. An analytic component of the visualization is the automatic positioning of the nodes on the screen, i.e., the projection of the graph onto a plane. Different algorithms position the nodes based on the strength of the links between nodes or to agglomerate the nodes into groups of the same kind. Once displayed, the user typically has the ability to move nodes, modify node and link attributes, zoom in, collapse, highlight, hide or delete portions of the graph.

Variable Creation

Link analysis can append new fields to existing records or create entirely new data sets for subsequent modeling stages in a decision system. For example, a new variable for a customer might be the total number of email addresses and credit card numbers linked to that customer.

Search

Link analysis query mechanisms include retrieving nodes and links matching specified criteria, such as node and link attributes, as well as search by example to find more nodes that are similar to the specified example node.

A more complex task is similarity search, also called clustering. Here, the objective is to find groups of similar nodes. These may actually be multiple instances of the same physical entity, such as a single individual using multiple accounts in a similar fashion.

Network Analysis

Network analysis is the search for parts of the linkage graph that play particular roles. It is used to build more robust communication networks and to combat organized crime. This exploration revolves around questions such as:

  • Which nodes are key or central to the network?
  • Which links can be severed or strengthened to most effectively impede or enhance the operation of the network?
  • Can the existence of undetected links or nodes be inferred from the known data?
  • Are there similarities in the structure of subparts of the network that can indicate an underlying relationship (e.g., modus operandi)?
  • What are the relevant sub-networks within a much larger network?
  • What data model and level of aggregation best reveal certain types of links and sub-networks?
  • What types of structured groups of entities occur in the data set?

Applications

Link analysis tools such as those provided by Crime Tech Solutions are increasingly used in law enforcement investigations, detecting terrorist threats, fraud detection, detecting money laundering, telecommunications network analysis, classifying web pages, analyzing transportation routes, pharmaceuticals research, epidemiology, detecting nuclear proliferation and a host of other specialized applications. For example, in the case of money laundering, the entities might include people, bank accounts and businesses, and the transactions might include wire transfers, checks and cash deposits. Exploring relationships among these different objects helps expose networks of activity, both legal and illegal.