Machine learning is a part of AI that looks at patterns of data and draws conclusions. Once it gets good at drawing the right decisions, it applies itself to new data sets to find hidden patterns. It is not a single method or technology, but rather a field of science that includes numerous technologies to create rules that can learn from the data in their environment and then make predictions and take actions when faced with a new situation.
A standard definition for ML is "The field of study that gives computers the ability to learn without being explicitly programmed." We give the computer large volumes of data and learn how to make choices about the data. For example, in rules-based extended car warranties claims validation, there is a pre-determined set of warranty laws, and the destruction of those rules may result in rejection or adjustment of the claim. In ML, the warranty rules are not pre-set in the system but learned through sample cases.
The information data set needs to be large and representative. The common assumption, supported by research, shows that depth and breadth of data are more impactful to ML model performance than the algorithms' cleverness. Section of the data is used to train the model, verify and optimize the model, and the rest for examining the model.
Feature extraction chooses the data fields to be practiced and adjusts them into a format fitting for the mathematical models in the ML algorithms. With warranty claim data, the plan selects the characteristics that could recognize a fraudulent customer, service agent, or claim. The model is then shaped by the algorithms, using the claims data, as defined by the extracted points. When training is finished, the initial model is approved and adjustments are made, resulting in the model becoming available to be used. ML operations cover the usage of the model, as well as setting it further over time.
The two main kinds of ML are supervised learning and unsupervised learning. The main distinction is that our input data includes information of what the output values should be in supervised learning. For instance, the training data for warranty requirements would include the claims validation issue - approved, adjusted/partially accepted, rejected, or on-hold for further confirmation. Training the directed learning model is a function that best approximates the wanted output with any given input.
On the other hand, unsupervised training does not have given outputs in the training information. It aims to learn the training data's inherent composition, cluster commonalities, and recognize exceptions. Unsupervised learning can find exceptions and anomalies in the data in warranty control and identify possible issues or suspected fraud.
Problems with Traditional Warranty Management
Although many businesses have considerably increased their warranty management methods and related system support, we still see common problems.
A rules-based claim administration approach often stays the same for extended periods and doesn't evolve at the same pace as fraud methods evolve. Over time, the assistance agents will learn which tricks are denied and which are approved and how to by-pass the commands. Many businesses struggle with the right number of rules, getting either too many false positives (hard for the honest service partners) or too many false negatives.
The sheer volume of warranty requires to be validated it difficult to configure the controls to the right level. Repeat, if too many claims need to be prepared manually, the validation lead time takes longer and the validator may in some cases, do mass-approvals to shorten the backlog. Having too many applications approved automatically can be expensive as well.
Relying on validation alone and not having good analytics to support it is not enough - it is always pleasant to generate fraudulent claims meeting all the laws, which are not caught.
Very often, claims administration teams experience high turnover rates, leading to variable skills and performance of the validation unit. In the worst-case situation, the system might flag claims for rejection but the team still allows them.
Why Is ML Suitable for Warranty Management?
ML is commonly applied in fraud detection and process mechanization. The education data for the ML model should be honest to get from most warranty claims administration applications.
Globally consistent warranty services can be done through process automation and helping claim handlers with analysis proposals to make better choices. Internal administration can be improved and the validator performance can be assessed against the ML models.
Process mechanization should also lead to quicker validation and operational savings.
With experienced validators, the model will continually learn from new cases and choices, so it will evolve at the same pace as new fraud schemes come up. Analytics applying anomaly detection may detect previously unknown fraud schemes.
Potential Measures to Apply ML in Warranty Management
There are undoubtedly many potential areas, where ML could be applied in warranty administration. One excellent example having a major influence on warranty costs is imminent analytics and maintenance. But, that is not in the right scope of warranty administration or this article.
Different area where ML can help out is with client entitlement and return material support, where it can help decide whether the case is a genuine warranty case and if it is safe to ship a spare part or a replacement goods to the customer in advance before getting the defective product or returned part.
Analytical claim getting analyses the possibility of fraud for each service agent and claim. ML fits well with that kind of classification problems. The effects can be used as input for the mechanical rules-based validation or for the claims validator for standard validation and handling of the continuing claims.
If there is enough repetitiveness and trust in the ML model, it could take care of the validation choices entirely or partially. For example, ML would recommend each claim and a determination factor for the recommendation. Clear examples would be handled automatically and cases with lower confidence would be directed to the rules-based validation or manual approach.
Internal control and validator performance
ML can be used for internal administration and to assess the claims validator display. The validation results would be matched against ML recommendations, and the performance of validators with big deviations would be encouraged to get it to the same level as the others.
Complementing warranty analytics
It is pretty easy to produce a few fraudulent warranty claims, which meet all standards and pass rules-based validation. But, it is very difficult to perform large-scale warranty fraud and remain statistically compatible with reality. With arithmetical analytics you can recognize individual claims, which have a high probability of being wrong. It can also assist in identifying service agents or clients who have a high number of these suspect claims and, therefore, applicants for further research.
Most known fraud techniques can be analyzed with conventional warranty analytics. Unsupervised ML and anomaly discovery can effectively detect unknown fraud and keep up with the evolving fraud techniques. They can also effectively bring up other ideas for higher costs, such as problems in service technician skills.
How to Get Started
In warranty administration, context ML is a new method, with little practical knowledge and skills available. Therefore, I wouldn't go "all-in" in the original project. I would rather begin with small pilots complementing and maintaining the existing processes and expand from that, once practice grows and confidence rises. Also, moving to higher mechanization levels involves many systems integration work with the existing guarantee systems, and associated process changes.
Once you have the first prototypes ready, it is essential to verify they are working perfectly. This is done by presenting the model new claims data which it hasn't seen earlier, where we understand the outcome. If the model behaves correctly, we can begin deploying it with real transactions.
The quality of the pattern can be measured by following the amount of false positives or false negatives. Both are dangerous for the business. False negatives indicate accepting fraudulent claims and extra cost, false positives imply rejecting valid cases, problems with service agents and extra hassle in the method.
Although these ideas are fairly new in warranty management, I believe ML and AI can provide the foundation for highly efficient warranty control and fraud detection, with clear benefits to complement traditional warranty methods.
A mixture of data science and warranty expertise must build an effective ML solution for warranty administration. Cloud-based solutions can offer a jump start with the easily available models and optimizers, but any business should also invest in developing internal expertise on the topic. Start investigating, capture the first advantages and build on that.