The use of Artificial Intelligence (AI) in financial services is all over the news, with some reports estimating it to be a US$450 billion opportunity. But what’s the real story

The post Artificial Intelligence as a Catalyst to Accelerate Financial Inclusion appeared first on Fintech Singapore.

Real-time AI/ML is on the rise and feature stores are key to successfully deploying them. Read on to see how the choice of online store and the feature store architecture play important roles in determining its performance and cost.

Organizational forms serve as a primary business tool across industries—from financial services, to healthcare, and more. Consider, for example, tax filing forms in the tax management industry, where new forms come out each year with largely the same information. AWS customers across sectors need to process and store information in forms as part of their […]

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that […]

Only 14 percent of check writers in US VC firms are female, highlighting the significant gap that remains between females and their male counterparts in the industry. Radhika Malik, a principal at Dell Technologies Capital (DTC), offers solutions to the VC gender gap.

In this post, we demonstrate how to create an automated email response solution using Amazon Comprehend. Organizations spend lots of resources, effort, and money on running their customer care operations to answer customer questions and provide solutions. Your customers may ask questions via various channels, such as email, chat, or phone, and deploying a workforce […]

Financial institutions are increasingly using automated models to accelerate credit decisions leveraging AI/ML. In order to build the next-generation models to handle these tasks, organizations must design systems that are reflective of the dynamics of a modern economy (prevalence of gig/flexible workers) and have the appropriate supporting data to assess. Argyle is an employment data platform that provides companies with real-time access to employment data that can be used in decisioning. All the data on the platform is user-permissioned, protecting the personal information of the applicant. In terms of employment, decisioning models have far too long stringently relied only on traditional W2 income for assessing worthiness, limiting access to the financial system for otherwise qualified potential borrowers. With Argyle, banking, lending, loan servicing, and insurance companies can get verified, real-time income data straight from the source. In addition to income and employment verification, the company also offers paycheck-linked lending, where loans are repaid directly from paycheck proceeds. The platform contains data and is integrated with 75% of the US workforce, covering 75M employees across 500K employers. AlleyWatch caught up with Argyle Founder and CEO Shmulik Fishman to learn more about how his experience in adtech served as the inspiration for the business, the company's strategic plans, latest round of funding, which brings the total funding raised to $77.6M, and much, much more...

    Trends related to cloud usage by financial services organizations (based on responses from the 154 survey respondents from financial services organizations) include: While “migrating more workloads to cloud” is the second-most prevalent cloud initiative across all survey respondents (Figure 25), it is the top initiative among financial services organizations, with 62% indicating plans […]

The post Cloud usage by financial svcs. orgs; new research from Flexera appeared first on Fintech News.

Machine learning (ML) helps organizations increase revenue, drive business growth, and reduce cost by optimizing core business functions across multiple verticals, such as demand forecasting, credit scoring, pricing, predicting customer churn, identifying next best offers, predicting late shipments, and improving manufacturing quality. Traditional ML development cycles take months and require scarce data science and ML […]

Many software as a service (SaaS) providers across various industries are adding machine learning (ML) and artificial intelligence (AI) capabilities to their SaaS offerings to address use cases like personalized product recommendation, fraud detection, and accurate demand protection. Some SaaS providers want to build such ML and AI capabilities themselves and deploy them in a […]

Amazon SageMaker Autopilot is an automated machine learning (AutoML) solution that performs all the tasks you need to complete an end-to-end machine learning (ML) workflow. It explores and prepares your data, applies different algorithms to generate a model, and transparently provides model insights and explainability reports to help you interpret the results. Autopilot can also […]

Data lakes have become the norm in the industry for storing critical business data. The primary rationale for a data lake is to land all types of data, from raw data to preprocessed and postprocessed data, and may include both structured and unstructured data formats. Having a centralized data store for all types of data […]