Skip to main content

Fraud Analytics with Hadoop

Fraud analysis has been one of the oft quoted use cases for Hadoop. We look at the topic further to explore usage of Hadoop ecosystem products. 


Per se, the fraud analytics can be divided into 3 further use cases:
1- Fraud detection: determining if a fraud is taking place or has occurred in the past and generating appropriate alert for it.
2- Fraud prevention: implementing controls and access to prevent fraud.
3- Fraud reduction: monitoring and predicting patterns to minimize chances of fraud occurrence


Listed below are some of the methods that can be implemented using Hadoop to ensure fulfillment of either of the 3 use cases above.
1- Deduplication -
            a) Entity matching - This could include exact or similar matching of entities like name, father name or contact information (phone, e-mail id, street, city) or phonetic matches using the deduplication methods. Since this is a data intensive exercise and requires matching previously built index, there cannot be better technology fit than Hadoop.
            b) Social network identity matching - Not very commonly used, but emerging off late, is a tendency to match social network profiles with customer identity. While this technique could be quite effective provided you have the right social network data feeds, please be aware of privacy laws that may be applicable.

2- Outlier detection -
            A usual outlier will be a deviation from a common usage pattern of a customer or transaction set. Using custom machine learning algorithms or available libraries, we would tend to combine data to see any outlier points. Clustering, probabilistic distributions along with visualization techniques are more common methods to derive outliers.
These may be used in conjunction with techniques like path analysis, sessionization, tokenization and attribution. Regression, co-relation, averages and graph analysis may also be employed based on functional requirement.

3- Workflow -
            Transaction streaming, monitoring, alert forwarding, alert disposal and transaction blocking could be among a few steps that a custom workflow may implement in fraud management system. Considering the massive volume of transactions, a custom DSL workflow may be implemented on top of Hadoop.

Some of the key advantages, that we see with Hadoop usage in fraud management systems include, but not limited to:
1. Quick loading of data with tools like Flume
2. No need of defined schema and instead using custom scripts/ programs to explore data
3. Reducing need for Data warehouse to use raw multi structured data as-is
4. Faster processing of data which reduces fraud detection time frame
5. Elimination of DB overheads like index, backups


Further implementation evidence is needed to see if a Rule Engine can also be built on top of a DSL framework. Overall, we expect a hybrid architecture involving engine, streams, workflow, dashboard, portal and Hadoop based analytics in a comprehensive Fraud management system. Implementations will vary based on current architecture in the organization and tool set preference.

----------------------------------------------------------------------------
top image: wolf in sheep clothing; source: freedigitalphotos.net

Comments

Popular posts from this blog

Low latency SQL querying on HBase

HBase has emerged as one of the most popular NoSQL database offering distributed, versioned, non-relational tables hosted on commodity hardware. However, with a large set of users coming from a relational SQL world, it made sense to bring the SQL back in this NoSQL. With Apache Phoenix, database professionals get a convenient way to query HBase through SQL in a fast and efficient manner. Continuing our discussion with James Taylor, the founder of Apache Phoenix, we focus on the functional aspects of Phoenix in this second part of interaction.
Although Apache Phoenix started off with distinct low latency advantage, have the other options like Hive/Impala (integrated with HBase) caught up in terms of performance?
No, these other tools such as Hive and Impala have not invested in improving performance against HBase data, so if anything, Phoenix's advantage has only gotten bigger as our performance improves.  See this link for comparison of Apache Phoenix with Apache Hive and Cloudera Im…

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

Pricing models for Hadoop products

A look at the various pricing models adopted by the vendors in the Hadoop ecosystem. While the pricing models are evolving in this rapid and dynamic market, listed below are some of the major variations utilized by companies in the sphere.
1) Per Node:Among the most common model, the node based pricing mechanism utilizes customized rules for determining pricing per node. This may be as straight forward as pricing per name node and data node or could have complex variants of pricing based on number of core processors utilized by the nodes in the cluster or per user license in case of applications.
2) Per TB:The data based pricing mechanism charges customer for license cost per TB of data. This model usually accounts non replicated data for computation of cost.
3) Subscription Support cost only:In this model, the vendor prefers to give away software for free but charges the customer for subscription support on a specified number of nodes. The support timings and level of support further …