Long story, short: Machine Learning needs a torrent of transactional data which is labeled and accurate. Without these 2 elements, it cannot give accurate predictions.
Machine Learning is a subset of Artificial Intelligence – the larger technology with promising industrial applications.
There is so much of attention that this single piece of technology is receiving that sometimes, we wonder what is real and what is hyped.
Among the many things that machine learning is credited with is its ability to detect frauds and errors, especially in the Banking, Financial Services, and Insurance (BFSI) industry. The commonly agreed form of financial records and availability of structured data is what makes the industry a perfect fit for Machine Learning adoption. It is believed that the availability of structured data on a real-time basis makes it easy for the ML system to detect frauds.
However, even with the real-time data, there are challenges that the machine learning system would have to confront in fraud detection. The first among them is that it needs labeled data. The system needs a stream of labeled data of transactions that it can recognize as ‘fraud’ or ‘not fraud’. The system cannot identify on its own from transactional data which is a fraudulent transaction and which is a genuine transaction.
So does it take away the magical charm that Machine Learning was expected to have?
Absolutely, not. Machine Learning is still capable of doing wonders, like fraud detection from transactional data of credit cards. To become capable of that the system needs to be trained. It needs to be trained with a Machine Learning Model based on which it examines each transaction and identify it as fraud or not fraud.
Building the data model and choosing the right algorithm to examine it is one of the most crucial steps in machine learning implementation. It takes time, a lot of data, sample datasets called test data and a domain expert (who is a human) who can teach the system what resembles a fraud and what resembles a genuine transaction.
Further, the teaching is a continuous process, since fraudsters get inventive their techniques making old methods of fraud obsolete and new methods impossible for the system to detect.
You need an ML Data Model
To build the ML data model, the system needs to be built with several datasets from real-life scenarios. Each transaction has to be labeled as ‘fraud’ and ‘non-fraud’ so that the system can pick up patterns and build upon its fraud-detection capabilities.
These labels are referred to as target or target attribute. The ML algorithm finds patterns from the sample dataset and maps it to the target attribute. In other data models, the target attributes could be anything else other than ‘fraud’ and ‘non-fraud’.
For example, ‘spam’ or ‘not spam’, ‘wine’ or ‘beer’, ‘dog’ or ‘cat’, etc. As the system gains more input it builds up intelligence to recognize advanced classifications like ‘Is this input given by a human or by a bot?’
In the credit card fraud detection scenario, this will translate into, “Does this transaction resemble the previous transactions conducted by the customer?”, “Does it adhere to protocols laid down by the bank?” and similar models.
The Model Building Process Takes Time
There are several ingredients that go into building a vast Machine Learning Data model.
- A sample dataset for training
- Target attributes of the transactional data contained in the sample dataset
- Instructions to the system on how to react to each target attribute
- Other training parameters
Gathering all these inputs and making it relevant to the machine learning system takes time.
Also, the target attributes are often dynamic in nature. Each time a new form of fraud is reported, it has to be added to the list of target attributes which the system has to learn. Until the fraud is found and reports, there is a delay. During that span of time, the ML system would continue to pass similar fraudulent transactions as ‘non-fraud’.
Building A Capable Machine Learning system
To build a capable machine learning system, you need to pay close attention to the data that goes into it. There is also the need for domain expertise and experience in data analytics which is hard to come by. Perhaps, focusing on a small functional area with definite goals to learn from will give a good start.
Once the desired results are achieved, the system can be further scaled to other areas with a larger scope. Building a proof of concept and prototypes will help the business learn the nuances of building a capable machine learning system. That will help the business understand the untold challenges and the tangible benefits that machine learning can bring to the business.
Beyond the Hype of Machine Learning
Machine Learning throws open a galore of benefits for various industrial applications. But, the road to its successful implementation is not a straight road. There are several roadblocks en route. One such obstacle is training the system with a data model with accurate labeled data. With a well-equipped data pipeline, such labeled data will pave the way to actionable insights.