Financial Service (FS) providers must identify patterns and signals in a customer’s financial behavior to provide deeper, up-to-the-minute, insight into their affordability and credit risk. FS providers use these insights to improve decision making and customer management capabilities. Machine learning (ML) models and algorithms play a significant role in automating, categorising, and deriving insights from bank transaction data.
Experian publishes Categorisation-as-a-Service (CaaS) ML models that automate analysis of bank and credit card transactions, to be deployed in Amazon SageMaker. Driven by a suite of Experian proprietary algorithms, these models categorise a customer’s bank or credit card transactions into one of over 180 different income and expenditure categories. The service turns these categorised transactions into a set of summarised insights that can help a business better understand their customer and make more informed decisions. These insights provide a detailed picture of a customer’s financial circumstances and resilience by looking at verified income, expenditure, and credit behavior.
This blog demonstrates how financial service providers can introduce affordability verification and categorisation into their digital journeys by deploying Experian CaaS ML models on SageMaker. You don’t need significant ML knowledge to start using Amazon SageMaker and Experian CaaS.
Affordability verification and data categorisation in digital journeys
Product onboarding journeys are increasingly digital. Most financial service providers expect most of these journeys to initiate and complete online. An example journey would be consumers looking to apply for credit with their existing FS provider. These journeys typically involve FS providers performing affordability verification to ensure consumers are offered products they can afford. FS providers can now use Experian CaaS ML models available via AWS Marketplace to generate real-time financial insights and affordability verification for their customers.
Figure 1 depicts a typical digital journey for consumers applying for credit.
- Data categorisation for transactional data. Existing transactional data for current consumers is typically sourced from on-premises data sources into a data lake in the cloud. It is then prepared and transformed for processing and analytics. This analysis is done based on the FS provider’s existing consent in compliance with relevant data protection laws. Additional transaction information for other accounts not held by the lender can be sourced from Open Banking and categorised separately.
- Store categorised transactions. Background processes run a SageMaker batch transform job using the Experian CaaS Data Categorisation model to categorise this transactional data.
- Consumer applies for credit. Consumers use the FS providers’ existing front-end web, mobile, or any other digital channel to apply for credit.
- FS provider retrieves up-to-date insights. Insights are generated in real time using the Experian CaaS insights model deployed as endpoints in SageMaker and returned to the consumer-facing digital channel.
- FS provider makes credit decision. The channel app consolidates these insights to decide on product eligibility and drive customer journeys.
Deploying and publishing Experian CaaS ML models to Amazon SageMaker
Figure 2 demonstrates the technical solution for the customer journey described in the preceding section.
- Financial Service providers can use AWS Data Migration Service (AWS DMS) to replicate transactional data from their on-premises systems such as their core banking systems to Amazon S3. Customers can source this transactional data into a highly available and scalable data lake solution on AWS. Refer to AWS DMS documentation for technical details on supported database sources.
- FS providers can use AWS Glue, a serverless data integration service, to cleanse, prepare, and transform the transactional data into formats supported by the Experian CaaS ML models.
- FS providers can subscribe and download CaaS ML models built for SageMaker from the AWS Marketplace.
- These models can be deployed to SageMaker hosting services as a SageMaker endpoint for real-time inference. Endpoints are fully managed by AWS, and can be set up to scale on demand and deployed in a Multi-AZ model for resilience. FS providers can use Amazon API Gateway and AWS Lambda to make these endpoints available to their consumer-facing applications.
- SageMaker also supports a batch transform mode for ML models, which in this scenario will be used to precategorise transactional data. This mode is also useful for use cases that require nearly continuous and regular analysis such as a regular anti-fraud assessment.
- Consumer requests for a financial product such as a credit card on an FS provider’s digital channels.
- These requests invoke SageMaker endpoints, which use Experian CaaS models to derive real-time insights.
- These insights are used to further drive the customer’s product journey. CaaS models are pre-trained and can return insights within the latency requirements of most real-time digital journeys.
Security and compliance using CaaS
AWS Marketplace models are scanned by AWS for common vulnerabilities and exposures (CVE). CVE is a list of publicly known information about security vulnerability and exposure. For details on infrastructure security applied by SageMaker, see Infrastructure Security in Amazon SageMaker.
Data security is a key concern for FS providers and sharing of data externally is challenging from a security and compliance perspective. The CaaS deployment model described here helps address these challenges as data owned by the FS provider remains within their control domain and AWS account. There is no requirement for this data to be shared with Experian. This means the customer’s personal financial information is retained by the FS provider. FS providers cannot access the model code as it is running in a locked SageMaker environment.
AWS Marketplace models such as the Experian CaaS ML models are deployed in a network isolation mode. This ensures that the models cannot make any outbound network calls, even to other AWS services such as Amazon S3. SageMaker still performs download and upload operations against Amazon S3 in isolation from the model.
Implementing upgrades to CaaS ML models
ML model upgrades can be performed in place in Amazon SageMaker as vendors release newer versions of their models in AWS Marketplace. Endpoints can be set up in a blue/green deployment pattern to ensure that upgrades do not impact consumers and be safely rolled back with no business interruptions.
Automated categorisation of bank transaction data is now being used by FS providers as they start to realise the benefits it can bring to their business. This is being driven in part by the advent of Open Banking. Many FS providers have increased confidence in the accuracy and performance of automated categorisation engines. Suppliers such as Experian are providing transparency around their methodologies used to categorise data, which is also encouraging adoption.
In this blog, we covered how FS providers can introduce automated categorisation of data and affordability identification capabilities into their digital journeys. This can be done quickly and without significant in-house ML skills, using Amazon SageMaker and Experian CaaS ML models. SageMaker endpoints and batch transform capabilities enable the deployment of a highly scalable, secure, and extensible ML infrastructure with minimal development and operational effort.
Experian’s CaaS is available for use via the AWS Marketplace.