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Predictive Models in Customer Onboarding Risk

Reduce fraud, improve compliance and offer customers a better experience by leveraging AI and ML.
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As organizations navigate the artificial intelligence (AI) and machine learning (ML) landscape, you must look for those key areas where adding these technologies will enhance your business. If you are in financial services or insurance, onboarding new clients can be risky, so things that can reduce the risk are important.

With AI and ML, you can reduce your risk while improving accuracy, efficiency, and compliance. Let’s see how AI and ML can help you with onboarding risk.

What are the risks associated with onboarding?

Several problems can arise when onboarding clients. If issues can be avoided or minimized, everyone benefits. Some common issues include:

  • Incorrect/fake Identification
  • Incorrect/fake supplementary documentation
  • Stolen identity

How AI and ML limit your exposure

Here are some ways that you can limit your onboarding risk exposure using AI and ML:

  • Customer experience improvement

Onboarding personalization: The onboarding process can be tailored to the individual’s preferences, needs, potential products, and risk profiles using AI. This improves overall satisfaction while ensuring a thorough risk assessment.

Virtual assistants and chatbots: You can reduce the risk of errors using AI-drive chatbots that guide customers through the onboarding process prompting them with the proper questions and gathering the necessary information for their application.

  • Data integrity and quality

Detection of duplicates: You can identify and even merge duplicate records with AI. This can also play a role in duplicate or fraudulent submissions. Taking advantage of AI in this way reduces the risk of inconsistencies and errors in customer information.

Data cleansing: AI algorithms can be used to automatically standardize and clean data associated with prospective customers. Taking these steps can ensure that any information that is used for risk assessment is up-to-date and accurate.

  • Fraud detection and prevention

Detection of anomalies: With machine learning models trained on historical fraud data, you can analyze various patterns in the data provided by prospective customers to detect any unusual behavior that may indicate fraudulent activity. When found, you can confirm that your models are continuously learning by feeding this additional data into the model to help improve their accuracy.

Predictive analytics: You can use predictive models to assess the risk level of new customers using their profile information and behavior to help mitigate potential fraud in advance.

  • Decision support

Underwriting enhancements: AI can assist underwriting and loan officers by providing data-driven insights and recommendations that improve the accuracy of risk assessments or policy pricing.

Automated decisions: Using AI to automate decisions can free up human resources to focus on higher-risk cases that may require more hands-on analysis.

  • Risk assessment and management

Credit scores: Companies can take advantage of ML models to evaluate the creditworthiness of an applicant by analyzing several data points even social media posts, mobile phone patterns, and more that can lead to a more accurate risk assessment.

Behavior analytics: You can enable more proactive risk management if you use AI to analyze customer behavior to identify risk factors before taking the applicant on as a customer.

  • Improved and enhanced identity verification

Biometrics and facial recognition: There are now AI offerings that provide facial recognition that can verify identity reducing fraud risk.

Verification of documents: Authenticity of documents is crucial. With AI algorithms, documents like IDs, passports, and utility bills can be compared against known valid document types and security features.

  • Regulatory compliance

Real-time monitoring: You can employ AI systems to continuously monitor transactions and customer activity so that suspicious activity can be flagged quickly for further investigation.

These are just some of the ways that AI and ML can help minimize onboarding risk.

Predictive models example

To see an example of how AI and ML can minimize onboarding risk, let’s investigate an example using predictive analytics in an onboarding process.

Assume that you have a prospective customer with the following background:

  • Credit score: 600
  • Debt-to-Income (DTI) ratio: 30%
  • Annual salary: $68,500
  • Requested loan amount: $25,000

Let’s assume this individual is applying for a loan for $25,000 to be paid over 7 years at an APR of 16.99%. What we need to know is how risky it is to accept this client.

So, you’ll need a business process that gathers data from the customer, does some calculations, and then passes the specific variable values to a machine learning algorithm that provides us with a “good” or “bad” risk based on the information provided.

You can see a simple example below.

In this example, a REST Connector returns the credit score for the applicant along with the DTI ratio based on the credit information obtained.

After this, a machine learning model runs using our Hugging Face Connector that will take the following inputs:

  • Date of birth
  • Credit score
  • DTI ratio
  • Annual salary

For example, the Hugging Face model can run a Python program that uses the scikit-learn package. Using the LogisticRegression and predict functions, the model can predict the probability of the risk class for the applicant. Based on the model configuration and response, the output of this BPMN component can provide results such as green, yellow, or red indicating the risk level, where these values indicate:

  • Green: Minimal risk
  • Yellow: Possible risk
  • Red: High risk

The model can estimate the probability of each risk class for the applicant.

Based on the outcome of this risk assessment, you can take appropriate additional steps to review manually, send a rejection notification, or prepare loan documents.

This is a simple example of how AI and ML can help to enhance your risk management for customer onboarding. You can see something similar to this in action in this Tutorial: Financial Risk Analysis with DMN, AI, and BPMN video.

Incorporate AI and ML into your business processes

If you take the time to properly leverage AI and ML in your business processes, you can minimize risk resulting in reduced fraud, improved compliance, better customer experience and more efficient and accurate operations.

For more information about how AI can enhance your business process and Camunda’s use of AI in our products, please check out the following blogs:

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