Connect with us

AI

Cognitive document processing for automated mortgage processing

Avatar

Published

on

This post was guest authored by AWS Advanced Consulting Partner Quantiphi.

The mortgage industry is highly complex and largely dependent on documents for the information required across different stages in their business value chain. Day-to-day operations for mortgage underwriting, property appraisal, and mortgage insurance underwriting are heavily dependent on the comprehension of different types of documents. The slow pace of document transfer between different business units of an organization slows down the overall approval process, leading to poor customer experience.

The mortgage loan approval process usually takes multiple weeks because a multitude of user-submitted documents are scrutinized at each stage to assess the underlying risk. Organizations need the right information at the right time to increase operational efficiency and better document management.

In the wake of COVID-19, the mortgage industry is reeling under pressure to undergo a digital transformation to provide a better customer experience. Large companies are cutting down capital and operational expenditure to sustain operations. The need for operational efficiency is higher than ever.

This post analyzes the role of machine learning (ML) solutions in document extraction in the mortgage industry to enhance business operations.

We highlight the key aspects of Quantiphi’s document processing solution built on AWS, and unveil how it helped a US-based mortgage insurance company address document management challenges through artificial intelligence (AI) and ML techniques.

Quantiphi is an AWS Partner Network (APN) Advanced Consulting Partner with AWS competencies in Machine Learning, Financial Services, Data & Analytics, and DevOps. Quantiphi also has multiple AWS Service Delivery designations, recognizing its expertise in leveraging specific AWS services.

ML-based document extraction for the mortgage industry

Lenders usually have to manually sieve through large volumes of loan packages containing structured and unstructured information to classify documents and identify key information. The identified information is further used for risk assessment. Most of this key information is usually contained in paragraphs, key-value pairs, and tables.

These lenders usually receive loan packages in bulk containing different types of documents such as W2, tax statements, 1008 forms, and so on. Currently, people have to first classify these documents manually and extract the relevant information. Therefore, mortgage firms are looking for meaningful ways of incorporating cognitive capabilities and solutions into their existing mortgage processing pipeline to automate the identification of key information and facilitate easy risk scoring in order to develop operational excellence and reduce manual efforts.

Quantiphi’s cognitive document processing solution combines state-of-the-art AI and ML services from AWS with Quantiphi’s custom document processing models to digitize a wide variety of mortgage documents. Quantiphi’s solution leverages services like Amazon Textract, Amazon SageMaker, Amazon Comprehend, Amazon Kendra, and Amazon Augmented AI (Amazon A2I) to help mortgage firms extract information from structured and unstructured documents, classify them into document types, and further address needs around risk assessment through ML.

Document classification and information extraction

Mortgage underwriting is done to assess the underlying risk for each application by analyzing the multitudes of user-submitted documents, such as W2 or I9 forms, tax returns, loan application (1003) forms, underwriting transmittal (1008) forms, demographic addendum, credit reports, bank account statements, and paycheck stubs. For example, the underwriting transmittal (1008) form contains the summary of the key information used during the risk assessment such as monthly income, qualifying rate, property details, and occupancy status. Paycheck stubs are another example of such documents, used to understand a borrower’s income in order to be sure that the borrower is able to repay the loan.

Similarly, property appraisal documents such as chain of title document and deed documents (assignment, trust, quitclaim) along with the property appraisal report are used to complement the property valuation process. Deed documents are processed to establish ownership and legal rights to a property. For example, if the lender sells a mortgage loan to another lender, they need to issue an assignment of deed of trust to give the new lender the same legal rights to the property.

Based on the inherent structure of the different types of mortgage documents, we have defined three broad segments to classify these documents:

  • Structured documents, which are standard documents with some variations over the years and across different states. All values, check boxes, and tables are usually contained in predefined areas of the document.
  • Semi-structured documents, which don’t follow a standardized template strictly but have a similar format.
  • Unstructured documents, which don’t follow any defined format.

Structured documents

Examples of structured documents include the loan application (1003) form, underwriting transmittal summary (1008) form, verification of employment (1005) form, and W2 form.

Consider underwriting the transmittal summary (1008) form. Quantiphi’s solution uses the standardized 1008 document as a reference for training, which is then used for extraction (see the following screenshot).

Key information that can be extracted from 1008 includes borrower and co-borrower names, property address, SSN, sales price, and appraised value.

Semi-structured documents

Semi-structured documents include pay stubs, bank statements, credit reports, and loan estimates. Here, Quantiphi’s solution uses a generic key-value pair and table detection model to extract the relevant features. Searching for certain common keywords results in a more efficient extraction.

The following screenshot shows data extraction from a pay stub.

Key information that can be extracted from includes paid period, deductions, net pay, 401K summary, and more.

Unstructured documents

Unstructured documents include deeds documents, appraisal reports, and more. Consider the assignment of deed of trust. Quantiphi’s Solution uses custom NLP techniques like entity recognition and syntax analysis to extract information (see the following screenshot).

Key information that can be extracted from the assignment of deed of trust includes the date of assignment, assignor, assignee, executor name, principal sum, and more.

Quantiphi’s cognitive document processing solution

Quantiphi’s cognitive document processing solution works across all types of structured and unstructured mortgage documents. Some key aspects of Quantiphi’s solution are as follows:

  • Capture – Powered by Amazon Textract and SageMaker. This feature uses deep-learning based OCR to identify and extract information such as key-value pairs, check boxes, tables, and signatures for further consumption by risk scoring models.
  • Categorize – Powered by SageMaker and Amazon Comprehend. This feature includes the automated classification of various types of mortgage documents like loan application (1003) forms, underwriting transmittal summary (1008) forms, W2 forms, pay stubs, bank statements, and credit reports.
  • Call out – Powered by Amazon Textract. This feature converts scanned applications and supporting documents into digital (searchable) PDFs and highlights the important information in the document along with the bookmarks to assist the loan underwriters with quick navigation through the document and to consume the relevant information for the further decision-making process.
  • Redaction – Powered by Amazon Textract and Amazon Comprehend. This feature enables identification and redaction of PII and PCI data like addresses, phone numbers, and names.
  • Interpret – Powered by Amazon Kendra and Amazon Elasticsearch Service (Amazon ES). This feature offers tools for easy consumption like a contextual and keyword-based search engine. Users can directly perform a search on a repository of processed documents to retrieve the relevant information along with the corresponding document link.
  • Human in the loop – Powered by Amazon A2I. This feature allows you to review and edit the extracted information based on the confidence score against the ground truth through an augmented AI workflow. This manual feedback is then used for continuous improvement of the extraction results through active learning.

The extracted information can be further fed into a risk assessment module to enable risk scoring of submissions in which low-risk applications are auto-approved and high-risk applications are marked for human review.

Quantiphi’s cognitive document processing solution is capable of achieving over 90% accuracy, provides substantial cost reductions, and facilitates better visibility of the mortgage processing workflow while assuring faster processing.

Let’s look at how Quantiphi built this solution by using a combination of AI and ML services provided by AWS.

Components used in the architecture ensure that the complete solution remains robust and scalable while providing high performance and reliability to process the incoming workload of documents in a cost-effective manner.

Solution architecture

The following diagram illustrates the architecture of Quantiphi’s solution.

The architecture consists of the following elements:

  1. The UI is hosted on Amazon Simple Storage Service (Amazon S3) and Amazon CloudFront is used for web distribution to ensure low latency.
  2. Through the UI, the user can upload multiple types of documents to Amazon S3 and select use cases like information extraction, document searchability, document classification, entity recognition, and insight generation.
  3. AWS Batch carries out preprocessing and stores these preprocessed images back into Amazon S3. The metadata information is captured in Amazon Aurora.
  4. AWS Lambda invokes Amazon Textract.
  5. Amazon Textract performs OCR to extract information. When the OCR job is complete, Amazon Textract triggers an Amazon Simple Notification Service (Amazon SNS) notification to add the completed job to an Amazon Simple Queue Service (Amazon SQS) queue.
  6. Lambda receives the Amazon Textract output and stores it in Amazon S3.
  7. An Auto Scaling Amazon Elastic Compute Cloud (Amazon EC2) instance converts these scanned images into a digital (searchable) PDF and writes the output to Amazon S3. Depending on the selected use cases, it writes the message into the respective four postprocessing SQS queues.
  8. If the uploaded PDFs are digital, AWS Batch skips the pipeline to write directly to the four postprocessing SQS queues.
  9. The solution uses a document classifier Docker container to classify pages and documents into categories.
  10. The enterprise search Docker enables the contextual search engine with the Q&A option on document content, content snippet generation, and document ranking.
  11. A document entity recognition and insights Docker is used for keyword and entity recognition and highlighting, masking of confidential data, chronological distribution of information, summarization, and topic modeling.
  12. The information extraction Docker has two functions:
    1. If the uploaded documents match any pre-trained documents, it extracts information from the documents and presents them to the user via the UI.
    2. If the documents don’t match any pre-trained model documents, it calls SageMaker via Amazon API Gateway and extracts key-value pairs, tables, check boxes, signatures, and stamps.

Customer use case: US-based mortgage insurance company

For this post, we present a use case in which the client is a leading US-based mortgage insurance company with a suite of mortgage, risk, real estate, and title services.

The client had millions of scanned pages of mortgage documents containing both handwritten and typed content, which were manually parsed to extract information and classify them accordingly. Processing of new mortgage loans was extremely time-consuming due to manual handling of over 400 different document types.

Quantiphi solution

Quantiphi developed an AI virtual assistant that takes user-uploaded documents and automatically classifies the documents and pages contained in them into different categories, such as bank statements, credit reports, tax returns, and property tax bills and statements.

To augment the consumption of information, the solution highlights key entities (with bounding boxes) present in them. The user can review and edit the extraction results via a custom reviewer UI tool, which is then used for accuracy benchmarking and re-training purposes.

Amazon QuickSight was used to build an interactive dashboard for presenting accuracy metrics and reconciliation.

The solution successfully digitized the processing of more than 50 million pages yearly, with an accuracy of over 97% in classification and 90% in the extraction of more than 40 different data points like borrower’s name, loan amount, and so on.

Quantiphi succeeded in expanding the customer’s profit margin by lowering its document processing costs. Their processing efficiency was enhanced through quick and accurate extraction and detection of data while eliminating manual efforts to greatly reduce the loan processing time.

Summary

Traditional methods of mortgage loan processing are manual in nature and highly time-consuming. Customers are often asked to provide a large number of documents that lenders have to manually go through for assessment.

Quantiphi’s cognitive document processing solution expedites the process by automating information extraction from the documents. Mortgage companies can use Quantiphi’s solution to increase their operational efficiency and significantly reduce their mortgage processing time.

The content and opinions in this post are those of the third-party author and AWS is not responsible for the content or accuracy of this post.


About the Authors

Arnav Gupta is AWS Practice Head at Quantiphi.

Bhaskar Kalita is FSI Head at Quantiphi.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://aws.amazon.com/blogs/machine-learning/cognitive-document-processing-for-automated-mortgage-processing/

Artificial Intelligence

The future of e-commerce: Trends, tips, traps to avoid

Avatar

Published

on

Amazon is approaching its 30th anniversary, set to mark the milestone in 2024. The World Wide Web hits 35 the same year. E-commerce, the buying and selling of goods and services over the internet, has grown up — and it has gotten big. Worldwide e-commerce sales for the retail sector alone exceeded $4 trillion in 2020, according to eMarketer. The research firm expects the figure to hit $5 trillion in 2022. Global B2B e-commerce sales, meanwhile, hovered around $6.6 trillion in 2020, according to research firm Frost & Sullivan.

As the value of e-commerce has risen, so has the complexity of online transactions. E-commerce today means more than simply processing electronic payments and enabling internet sales. It’s also more than knowing your customer, crucial as that is. E-commerce sales in 2021 depend upon the robust performance of just about every aspect of modern enterprises, from operations and supply chain to delivery services and customer loyalty programs.

Organizations must harness all the power of integrated back-office systems in tandem with intelligent customer insight systems to deliver personalized, seamless digital transactions that — in the lingo of the age — delight the customer. Personalized, seamless transactions must happen whether the customer is an individual consumer buying his or her first product, or a global business ordering for the 100th time under a multiyear procurement contract. Buyers demand as much — whether they’re ordering from their computer or their smartphone, via Alexa or through another connected machine.

“E-commerce transactions are becoming ubiquitous, and expectations are going up. People have expectations that it’s always going to be as easy as using Uber to get a ride,” said Mike Welsh, chief creative officer at Mobiquity, a digital consulting agency.

Amid rising customer expectations, however, many organizations are falling short on their e-commerce operations. A Gartner report on the COVID-19 pandemic’s impact on digital commerce predicted that “through 2020, 50% of large organizations will have failed to unify engagement channels, resulting in a disjointed and siloed customer experience that lacks context.”

The bar is high, said Lisa Woodley, vice president of customer experience at NTT Data Services. “E-commerce [covers] every stage, from acquisition to loyalty and advocacy. It’s your customers telling their friends, ‘I had a great experience; go do business with this company.’”

In this look at the future of e-commerce, we examine the evolution of buying and selling over the internet — from the early corporate websites that functioned as online brochures to today’s powerful, concierge selling sites that can be accessed through multiple channels. We offer expert analysis of the impact of COVID-19 on digital transactions, delve into the challenges enterprises face in meeting customer expectations in 2021, and provide detailed advice on overcoming those challenges.

From ‘product-centric’ to ‘solution-centric,’ e-commerce evolves

A combination of factors is driving the evolution of e-commerce. At the core is the internet.

Companies once mostly competed on the so-called four P’s of marketing: place, price, product and promotion. But the web’s search function and the internet’s reach neutralize one or more of these differentiating factors. Shopping online, a customer can easily get the same or similar product at the same or lower price with comparable shipping times and costs.

As a result, other factors are emerging as key differentiators, with personalization being the catch-all term for the new elements that drive buying habits in the digital realm.

“The concept of e-commerce is shifting from online sale transactions [and a] static webpage to a personalized and interactive experience,” said Eleftheria Kouri, a research analyst with the tech market advisory firm ABI Research.

“Customers have access to a wider range of capabilities when visiting an online store, including product virtual try-on and gaming and interactive storytelling concepts that increase engagement and educate the consumer about products [and] brands.”

Penny Gillespie, vice president at Gartner and a fellow in its customer experience/digital commerce team, said that in the e-commerce marketplace of 2021, companies must figure out how to deliver the product and the solution to a customer’s problem. To do that, they must understand the online customer’s intent.

For example, a retailer serving a customer searching for a black dress should be capable of using digital tools, as well as general and personal data, to understand that the shopper doesn’t simply need a dress but rather needs an outfit for an event. In fact, the color of the dress may in this case be irrelevant — with black dress being nearly synonymous with cocktail dress.

“Understanding intent is part of personalizing an experience,” Gillespie explained. A retailer that understands this concept can ensure the products in the search results actually match that customer’s needs, guaranteeing the sale of a dress and other relevant items (e.g., accessories) — and ensuring repeat business.

Customer intent is relevant in B2B transactions as well. Here, it could mean understanding a customer’s unique procurement process by, for example, automatically displaying any special prices specified by an existing procurement contract, facilitating any approval requirements, and anticipating needs based on past ordering histories.

“It’s a work in progress, with some sellers being much better at it than others,” Gillespie said.

In both the B2C and B2B spaces, online selling has gone from being reactive to being proactive and participatory, said Gillespie: “It’s a move from being product-centric to solution-centric.” She used the sale of an exercise bike online as an example.

“It’s not just selling an exercise bike online, but rather delivering it to the buyer’s house, setting it up and then helping them maximize its value through use,” she said. “The bike is a product; when it’s in my house and working, it is the solution.”

COVID-19 pushes companies and customers into the digital realm

The evolution of e-commerce from static webpages to interactive customer “solution” sites was enabled by sophisticated technology, but it took a global health crisis to make the future of e-commerce the new normal. The shift to online-everything in 2020 due to pandemic-induced social restrictions and quarantine orders pushed physical transactions into the digital realm.

According to findings from consulting firm McKinsey & Company, e-commerce as a percentage of overall retail sales in the U.S. grew 3.3 times more in 2020 than the average annual rate in five years before COVID-19. E-commerce sales as a share of overall retail sales grew 4.6% in 2020 vs. an average of 1.4% growth in previous years.

“Consumers are demanding more digital access than ever before,” said Nicole West, vice president of digital strategy and product at Chipotle Mexican Grill.

In November, the restaurant chain opened its first “digital-only restaurant,” the Chipotle Digital Kitchen, in Highland Falls, N.Y. The location offers pickup and delivery only, a prototype the company said will allow Chipotle to enter more urban areas that don’t support its full-size restaurant concept. The new restaurant requires customers to order in advance via Chipotle.com, its app or through third-party delivery partner platforms.

Providing an exceptional digital experience has become a priority for the 28-year-old chain, West said. She added that Chipotle is “relentless when it comes to UX and making it fast, easy and convenient” to place digital orders.

She cited the company’s 2020 rollout of Unlimited Customization. A feature in the Chipotle app and on the company’s website, it allows customers to customize orders, just as they do when ordering in person at a restaurant. Earlier in 2020, the company launched ordering on Facebook Messenger and a Group Ordering feature, which allows multiple people to participate in the ordering process simultaneously on the Chipotle app and Chipotle.com. And it’s now testing Chipotle Carside at 29 restaurants in California, an in-app feature that lets customers have their Chipotle orders delivered to their parked cars.

Chipotle’s digitalization efforts have shown real-world business value. Digital sales for Chipotle have grown 177% year over year, West said, and they accounted for 49% of sales in the last quarter of 2020. More than 19 million people joined the company’s customer rewards program via digital sales, West added, noting that the company’s digital pickup orders are currently its most lucrative transaction type.

E-commerce trends

The appetite for digital access is unlikely to abate. The McKinsey report cited above noted that approximately three-quarters of people who used digital channels for the first time during the pandemic said they plan to continue using them when normalcy returns.

There is no denying that the COVID-19 crisis and the at-home new norm have reshaped consumer behavior and boosted e-commerce/online shopping, which is expected to continue growing after the end of the pandemic, ABI Research’s Kouri said.

The technology powering these e-commerce trends also continues to evolve rapidly, Kouri noted, citing technological advancements in smartphones — such as high-resolution cameras and displays — enhanced connectivity, mobile-friendly websites and the rise of social media shopping.

Amazon, of course, has continued to make online shopping easier with innovations such as its Add to Cart and Buy Now buttons. The Home Depot and Lowe’s are often lauded for their use of instructional videos that give customers confidence to make purchases, as well as for apps that help customers navigate their stores. And the use of various technologies to let customers see how their items will look on them or in their homes before they buy is becoming standard practice.

From the customer’s perspective, Gartner’s Gillespie noted, the benchmark for all digital transactions is “the last great experience they had.” Keeping up with that moving target will require a panoply of technology and continuous technology innovation.

E-commerce technologies

Although the internet was the enabling technology for e-commerce, it is far from the only technology needed to deliver the experience that customers expect now and moving forward. Some of these broad technology capabilities include the following:

  • Customer-facing capabilities. Sites must be easy to navigate and user-friendly as well as quick and responsive. Sites should have the features that matter most to the target audience and be able to interact with other sites — social media sites for young consumers, for example, or company procurement systems for corporate customers.
  • Data-related technologies. Organizations must be capable of collecting and using their own data as well as data from outside sources. This allows the organization to anticipate a customer’s needs even when it has little or no data on that specific customer; the company can use its other data sources to compile an understanding of what that one customer needs based on its interactions with similar customers.
  • Automation technologies such as RPA. Robotic process automation can speed and streamline processes that service the customer by minimizing errors in data collection, enabling self-service by providing access to back-end systems.
  • Customer journey orchestration engine software. This class of tools help organizations analyze real-time data of individual customers to predict future interactions with that customer, using predictive models, decision trees, matrix rules and machine learning.
  • Augmented reality. AR lets customers bring products into real lifelike situations and virtually try on or fit items before purchasing. “The introduction of digital tools, such as augmented reality, in e-commerce platforms or apps not only assists brands to differentiate from the competition but transfers static websites/2D images to interactive and personalized experiences,” Kouri said.
  • Artificial intelligence. Organizations can use AI to offer personalized online experiences. A cosmetics brand, for instance, could use AI algorithms to provide skin analysis and recommend suitable products.
  • Back-end systems. Companies need modern infrastructure and current IT architecture that can support all these other capabilities. Typically, this means moving from legacy systems to cloud computing and SaaS applications to quickly enable scale and speed when needed; leveraging microservices to increase agility and flexibility; and breaking down silos through integration and the use of APIs. “There’s actually a lot more on the back end needed to reach our goal of making the front-end experience as seamless as possible,” Woodley said.

Specific tools, such as geofencing platforms that provide location-based services to help organizations and customers pinpoint their locations, and payment systems also have an important role in an e-commerce strategy, as do the technologies and processes companies use to optimize their warehouse and supply chain management.

“Building competitive e-commerce experiences requires the synergy of numerous technologies and tools, from AR to AI and secure payment systems,” summed up Kouri.

E-commerce challenges

Bringing all these parts together to work consistently and flawlessly is, not surprisingly, a significant challenge.

“Personalization is not a one-size-fits-all approach. You really need to consider your business model, value proposition and customers before you create your strategy. Once those pieces are solidified, you can then begin to seek out the right tools and technologies needed to be successful,” said Britt Mills, senior director of customer experience at Mobiquity.

Organizations also need the data experts, technologists, marketing team, logistics workers, supply chain personnel and other executives and supporting staff who can competently contribute to that vision.

“Stores can rush to market with a new technology to enable customer safety and convenience, but they shouldn’t do so at the customer’s expense,” Mills said. Training employees to use the new technology is essential. “If your associates can’t support this new expected experience, your customers won’t be satisfied. It doesn’t matter how great the technology is.”

In addition, companies must have a strategy for dealing with emerging data use laws that put more control over personal information into the hands of individuals. And they must be able to mitigate against escalating cybersecurity risks.

These capabilities and safeguards are hard to achieve. Experts have acknowledged that the difficulty of developing and implementing know-your-customer processes — from collecting the necessary data to analyzing it to turning it into action items — has been oversimplified and glossed over in many conversations.

It’s not shocking, then, to learn that most organizations are struggling to develop the capabilities required to deliver seamless, personalized service, especially as the number of engagement and delivery channels have increased.

Research from Verint, a provider of customer engagement management products, found that 82% of the nearly 2,300 business leaders it surveyed said the challenges of managing customer engagement will increase in 2021, but only 50% said they’re well prepared to support customer engagement priorities moving forward. The vast majority of those surveyed pointed to nearly every aspect of customer engagement as challenging for their organizations, indicating the following problems:

  • understanding and acting on rapidly changing customer behaviors (94% cited);
  • managing the growth in volume of customer interactions (88%);
  • achieving a unified view of customer engagement and overcoming data silos (79%);
  • using customer feedback to improve experiences (78%); and
  • building enduring customer relationships (77%).

Customer journey mapping

How do traditional organizations become as competitive in the digital sphere as they were in the brick-and-mortar heyday? It starts with mastering customer journey mapping, according to Peter Charness, vice president of retail strategy for UST, a digital technology and transformation IT services and solutions firm.

“[Organizations] need to ensure the digital journey is well aligned to a shopper’s needs, using a high degree of personalization and creating relevant interactions and conversations,” Charness said. He laid out six areas where organizations need to benchmark their capability:

  • Interest generation. Determine how successful your organization is at getting potential customers to its website or store.
  • Research and decision influence. Examine whether it’s easy for the user to find products of interest and “gather the information and confidence they need to move that product into a shopping cart,” Charness said.
  • Decision confidence. From browsing to buying, companies should make it easy for the shopper to say yes to a purchase. “Organizations should have this part of the conversation with their shoppers and build their confidence to press ‘buy,’”Charness noted.
  • Delivery/collection. “Speed of delivery (with ease of return implied) comes next, and the cost to deliver or collect a product becomes one of the most relevant associations any retailer will have to profitability and customer satisfaction,” Charness said. Assess your supply chain and fulfillment capabilities, and benchmark them to competitors and best-in-class companies.
  • Post-sales service, resales and loyalty. “Your conversation with your customer doesn’t end with the shipment,” Charness Consider what else you can say to or advise your customer on to keep the relationship alive and productive.
  • Personalization everywhere. “Put yourself in your shopper’s shoes, and play back the conversations you’ve had during the entire shopper journey,” Charness said. “Was it always relevant, interesting and useful? Or did you communicate with mass marketing techniques, treating everyone the same?” Develop a strategy for using AI and machine learning across “the end-to-end interaction chain” with customers to enhance personalized service.

The future of e-commerce

Many businesses have been on their e-commerce journey for years, adapting business processes to the customer predilection for digital transactions. However, few were well prepared for the rapid and wholesale shift to digital transactions driven by the pandemic. A record-breaking 11,100-plus stores closed in the U.S. last year, and 40 major retailers filed for Chapter 11 bankruptcy protection, according to CoStar Group, a collector of retail real estate data. More stores are expected to shutter in the upcoming years, with some analysts predicting 100,000 stores — mostly apparel retailers — could close by 2025.

Yet, despite their struggles and challenges, many organizations are on their way to success. The awareness that challenges must be faced and addressed indicates that organizations understand that data-driven, personalized and secure customer transactions are the future.

How these transactions happen — whether online, via a mobile device, through some combination of digital and physical channels or by some augmented reality lens not yet imagined — will depend on circumstances and customer preferences, but they will increasingly involve digital technologies.

Indeed, Gartner has advocated replacing the term e-commerce with digital commerce to better reflect the convergence of all the digital systems that go into transactions today.

As customers increasingly decide that the frictionless experiences they have when buying online from leading digital vendors are the norm, the semantic distinction between e-commerce or digital commerce or any other kind of buying and selling transaction will disappear.

“When it’s all said and done,” Gillespie said, “we’ll just call it commerce.”

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.fintechnews.org/the-future-of-e-commerce-trends-tips-traps-to-avoid-2/

Continue Reading

AI

France’s Shift Technology, an SaaS Provider of AI based Decision Automation for Insurance, Secures $220M via Series D

Avatar

Published

on

France-based Shift Technology, an SaaS provider of AI-enhanced decision automation and optimization solutions for the insurance sector, recently revealed that it has finalized a $220 million Series D funding round.

Shift Technology‘s latest investment round brings total investment in the company to $320 million along with a market valuation of over $1 billion. This investment reportedly marks Advent’s sixth growth equity investment in 2021. Shift’s round was led by Advent International, via Advent Tech, along with contributions from Avenir and other investors.

Previous Series C investors Accel, Bessemer Venture Partners, General Catalyst, and Iris Capital also took part in Shift’s Series D round.

With this latest funding, Shift said it would use the capital to expand its business operations into the US, Europe, and Asia as well.

In the United States, the firm will be penetrating the property and casualty (P&C) insurance sector and will also expand into the health insurance industry, an area in which the company sees a great opportunity.

The funds raised by Shift Technology will also be used to support researach and development (R&D) work in the implementation of new solutions to cater to innovative decision automation and optimization needs for insurers.

Initially known for its fraud detection and claims automation solutions, in January 2021 Shift Technology launched its Insurance Suite to enable insurance providers to leverage AI-powered decision automation and optimization tech to a wider array of critical processes (related to policy lifecycle, including underwriting, subrogation, and compliance).

The firm currently serves over 100 clients in 25 countries and has reportedly analyzed almost 2 billion claims so far.

Thomas Weisman, a Director on Advent’s technology investment in London, stated:

“Since its founding in 2014, Shift has made a name for itself in the complex world of insurance.Shift’s advanced suite of SaaS products is helping insurers to reshape manual and often time-consuming claims processes in a safer and more automated way. We are proud to be part of this exciting company’s next wave of growth.”

Jeremy Jawish, CEO and co-founder, Shift Technology, remarked:

“We are thrilled to partner with Advent International, given their considerable sector expertise and global reach and are taking another giant step forward with this latest investment. We have only just scratched the surface of what is possible when AI-based decision automation and optimization is applied to the critical processes that drive the insurance policy lifecycle.”

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.crowdfundinsider.com/2021/05/175107-frances-shift-technology-an-saas-provider-of-ai-based-decision-automation-for-insurance-secures-220m-via-series-d/

Continue Reading

Artificial Intelligence

Longevity startup Gero AI has a mobile API for quantifying health changes

Avatar

Published

on

Sensor data from smartphones and wearables can meaningfully predict an individual’s ‘biological age’ and resilience to stress, according to Gero AI.

The ‘longevity’ startup — which condenses its mission to the pithy goal of “hacking complex diseases and aging with Gero AI” — has developed an AI model to predict morbidity risk using ‘digital biomarkers’ that are based on identifying patterns in step-counter sensor data which tracks mobile users’ physical activity.

A simple measure of ‘steps’ isn’t nuanced enough on its own to predict individual health, is the contention. Gero’s AI has been trained on large amounts of biological data to spots patterns that can be linked to morbidity risk. It also measures how quickly a personal recovers from a biological stress — another biomarker that’s been linked to lifespan; i.e. the faster the body recovers from stress, the better the individual’s overall health prognosis.

A research paper Gero has had published in the peer-reviewed biomedical journal Aging explains how it trained deep neural networks to predict morbidity risk from mobile device sensor data — and was able to demonstrate that its biological age acceleration model was comparable to models based on blood test results.

Another paper, due to be published in the journal Nature Communications later this month, will go into detail on its device-derived measurement of biological resilience.

The Singapore-based startup, which has research roots in Russia — founded back in 2015 by a Russian scientist with a background in theoretical physics — has raised a total of $5 million in seed funding to date (in two tranches).

Backers come from both the biotech and the AI fields, per co-founder Peter Fedichev. Its investors include Belarus-based AI-focused early stage fund, Bulba Ventures (Yury Melnichek). On the pharma side, it has backing from some (unnamed) private individuals with links to Russian drug development firm, Valenta. (The pharma company itself is not an investor).

Fedichev is a theoretical physicist by training who, after his PhD and some ten years in academia, moved into biotech to work on molecular modelling and machine learning for drug discovery — where he got interested in the problem of ageing and decided to start the company.

As well as conducting its own biological research into longevity (studying mice and nematodes), it’s focused on developing an AI model for predicting the biological age and resilience to stress of humans — via sensor data captured by mobile devices.

“Health of course is much more than one number,” emphasizes Fedichev. “We should not have illusions about that. But if you are going to condense human health to one number then, for a lot of people, the biological age is the best number. It tells you — essentially — how toxic is your lifestyle… The more biological age you have relative to your chronological age years — that’s called biological acceleration — the more are your chances to get chronic disease, to get seasonal infectious diseases or also develop complications from those seasonal diseases.”

Gero has recently launched a (paid, for now) API, called GeroSense, that’s aimed at health and fitness apps so they can tap up its AI modelling to offer their users an individual assessment of biological age and resilience (aka recovery rate from stress back to that individual’s baseline).

Early partners are other longevity-focused companies, AgelessRx and Humanity Inc. But the idea is to get the model widely embedded into fitness apps where it will be able to send a steady stream of longitudinal activity data back to Gero, to further feed its AI’s predictive capabilities and support the wider research mission — where it hopes to progress anti-ageing drug discovery, working in partnerships with pharmaceutical companies.

The carrot for the fitness providers to embed the API is to offer their users a fun and potentially valuable feature: A personalized health measurement so they can track positive (or negative) biological changes — helping them quantify the value of whatever fitness service they’re using.

“Every health and wellness provider — maybe even a gym — can put into their app for example… and this thing can rank all their classes in the gym, all their systems in the gym, for their value for different kinds of users,” explains Fedichev.

“We developed these capabilities because we need to understand how ageing works in humans, not in mice. Once we developed it we’re using it in our sophisticated genetic research in order to find genes — we are testing them in the laboratory — but, this technology, the measurement of ageing from continuous signals like wearable devices, is a good trick on its own. So that’s why we announced this GeroSense project,” he goes on.

“Ageing is this gradual decline of your functional abilities which is bad but you can go to the gym and potentially improve them. But the problem is you’re losing this resilience. Which means that when you’re [biologically] stressed you cannot get back to the norm as quickly as possible. So we report this resilience. So when people start losing this resilience it means that they’re not robust anymore and the same level of stress as in their 20s would get them [knocked off] the rails.

“We believe this loss of resilience is one of the key ageing phenotypes because it tells you that you’re vulnerable for future diseases even before those diseases set in.”

“In-house everything is ageing. We are totally committed to ageing: Measurement and intervention,” adds Fedichev. “We want to building something like an operating system for longevity and wellness.”

Gero is also generating some revenue from two pilots with “top range” insurance companies — which Fedichev says it’s essentially running as a proof of business model at this stage. He also mentions an early pilot with Pepsi Co.

He sketches a link between how it hopes to work with insurance companies in the area of health outcomes with how Elon Musk is offering insurance products to owners of its sensor-laden Teslas, based on what it knows about how they drive — because both are putting sensor data in the driving seat, if you’ll pardon the pun. (“Essentially we are trying to do to humans what Elon Musk is trying to do to cars,” is how he puts it.)

But the nearer term plan is to raise more funding — and potentially switch to offering the API for free to really scale up the data capture potential.

Zooming out for a little context, it’s been almost a decade since Google-backed Calico launched with the moonshot mission of ‘fixing death’. Since then a small but growing field of ‘longevity’ startups has sprung up, conducting research into extending (in the first instance) human lifespan. (Ending death is, clearly, the moonshot atop the moonshot.) 

Death is still with us, of course, but the business of identifying possible drugs and therapeutics to stave off the grim reaper’s knock continues picking up pace — attracting a growing volume of investor dollars.

The trend is being fuelled by health and biological data becoming ever more plentiful and accessible, thanks to open research data initiatives and the proliferation of digital devices and services for tracking health, set alongside promising developments in the fast-evolving field of machine learning in areas like predictive healthcare and drug discovery.

Longevity has also seen a bit of an upsurge in interest in recent times as the coronavirus pandemic has concentrated minds on health and wellness, generally — and, well, mortality specifically.

Nonetheless, it remains a complex, multi-disciplinary business. Some of these biotech moonshots are focused on bioengineering and gene-editing — pushing for disease diagnosis and/or drug discovery.

Plenty are also — like Gero —  trying to use AI and big data analysis to better understand and counteract biological ageing, bringing together experts in physics, maths and biological science to hunt for biomarkers to further research aimed at combating age-related disease and deterioration.

Another recent example is AI startup Deep Longevity, which came out of stealth last summer — as a spinout from AI drug discovery startup Insilico Medicine — touting an AI ‘longevity as a service’ system which it claims can predict an individual’s biological age “significantly more accurately than conventional methods” (and which it also hopes will help scientists to unpick which “biological culprits drive aging-related diseases”, as it put it).

Gero AI is taking a different tack toward the same overarching goal — by honing in on data generated by activity sensors embedded into the everyday mobile devices people carry with them (or wear) as a proxy signal for studying their biology.

The advantage being that it doesn’t require a person to undergo regular (invasive) blood tests to get an ongoing measure of their own health. Instead our personal device can generate proxy signals for biological study passively — at vast scale and low cost. So the promise of Gero’s ‘digital biomarkers’ is they could democratize access to individual health prediction.

And while billionaires like Peter Thiel can afford to shell out for bespoke medical monitoring and interventions to try to stay one step ahead of death, such high end services simply won’t scale to the rest of us.

If its digital biomarkers live up to Gero’s claims, its approach could, at the least, help steer millions towards healthier lifestyles, while also generating rich data for longevity R&D — and to support the development of drugs that could extend human lifespan (albeit what such life-extending pills might cost is a whole other matter).

The insurance industry is naturally interested — with the potential for such tools to be used to nudge individuals towards healthier lifestyles and thereby reduce payout costs.

For individuals who are motivated to improve their health themselves, Fedichev says the issue now is it’s extremely hard for people to know exactly which lifestyle changes or interventions are best suited to their particular biology.

For example fasting has been shown in some studies to help combat biological ageing. But he notes that the approach may not be effective for everyone. The same may be true of other activities that are accepted to be generally beneficial for health (like exercise or eating or avoiding certain foods).

Again those rules of thumb may have a lot of nuance, depending on an individual’s particular biology. And scientific research is, inevitably, limited by access to funding. (Research can thus tend to focus on certain groups to the exclusion of others — e.g. men rather than women; or the young rather than middle aged.)

This is why Fedichev believes there’s a lot of value in creating a measure than can address health-related knowledge gaps at essentially no individual cost.

Gero has used longitudinal data from the UK’s biobank, one of its research partners, to verify its model’s measurements of biological age and resilience. But of course it hopes to go further — as it ingests more data. 

“Technically it’s not properly different what we are doing — it just happens that we can do it now because there are such efforts like UK biobank. Government money and also some industry sponsors money, maybe for the first time in the history of humanity, we have this situation where we have electronic medical records, genetics, wearable devices from hundreds of thousands of people, so it just became possible. It’s the convergence of several developments — technological but also what I would call ‘social technologies’ [like the UK biobank],” he tells TechCrunch.

“Imagine that for every diet, for every training routine, meditation… in order to make sure that we can actually optimize lifestyles — understand which things work, which do not [for each person] or maybe some experimental drugs which are already proved [to] extend lifespan in animals are working, maybe we can do something different.”

“When we will have 1M tracks [half a year’s worth of data on 1M individuals] we will combine that with genetics and solve ageing,” he adds, with entrepreneurial flourish. “The ambitious version of this plan is we’ll get this million tracks by the end of the year.”

Fitness and health apps are an obvious target partner for data-loving longevity researchers — but you can imagine it’ll be a mutual attraction. One side can bring the users, the other a halo of credibility comprised of deep tech and hard science.

“We expect that these [apps] will get lots of people and we will be able to analyze those people for them as a fun feature first, for their users. But in the background we will build the best model of human ageing,” Fedichev continues, predicting that scoring the effect of different fitness and wellness treatments will be “the next frontier” for wellness and health (Or, more pithily: “Wellness and health has to become digital and quantitive.”)

“What we are doing is we are bringing physicists into the analysis of human data. Since recently we have lots of biobanks, we have lots of signals — including from available devices which produce something like a few years’ long windows on the human ageing process. So it’s a dynamical system — like weather prediction or financial market predictions,” he also tells us.

“We cannot own the treatments because we cannot patent them but maybe we can own the personalization — the AI that personalized those treatments for you.”

From a startup perspective, one thing looks crystal clear: Personalization is here for the long haul.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://techcrunch.com/2021/05/07/longevity-startup-gero-ai-has-a-mobile-api-for-quantifying-health-changes/

Continue Reading

AI

7 Ways Artificial Intelligence is Improving Healthcare

Avatar

Published

on

Emerging technologies have the potential to completely reshape the healthcare industry and the way people manage their health. In fact, tech innovation in healthcare and the use of artificial intelligence (AI) could provide more convenient, personalized care for patients.

It could also create substantially more value for the industry as a whole—up to $410 billion per year by 2025.

This graphic by RYAH MedTech explores the ways that technology, and more specifically AI, is transforming healthcare.

How is Technology Disrupting the Patient Experience?

Tech innovation is emerging across a wide range of medical applications.

Because of this, AI has the potential to impact every step of a patient’s journey—from early detection, to rehabilitation, and even follow-up appointments.

Here’s a look at each step in the patient journey, and how AI is expected to transform it:

1. Prevention

Wearables and apps track vast amounts of personal data, so in the future, AI could use that information to make health recommendations for patients. For example, AI could track the glucose levels of patients with diabetes to provide personalized, real-time health advice.

2. Early Detection

Devices like smartwatches, biosensors, and fitness trackers can monitor things like heart rate and respiratory patterns. Because of this, health apps could notify users of any abnormalities before conditions become critical.

Wearables could also have a huge impact on fall prevention among seniors. AI-enabled accelerometer bracelets and smart belts could detect early warning signs, such as low grip strength, hydration levels, and muscle mass.

3. Doctors Visits

A variety of smart devices have the potential to provide support for healthcare workers. For instance, voice technology could help transcribe clinical data, which would mean less administrative work for healthcare workers, giving them more time to focus on patient care.

Virtual assistants are expected to take off in the next decade. In fact, the healthcare virtual assistant market is projected to reach USD $2.8 billion by 2027, at a CAGR of 27%.

4. Test Results

Traditionally, test results are analyzed manually, but AI has the potential to automate this process through pattern recognition. This would have a significant impact on infection testing.

5. Surgery / Hospital Visits

Research indicates that the use of robotics in surgery can save lives. In fact, one study found that robot assisted kidney surgeries saw a 52% increase in success rate.

Robotics can also support healthcare workers with repetitive tasks, such as restocking supplies, disinfecting patient rooms, and transporting medical equipment, which gives healthcare workers more time with their patients.

6. Rehabilitation

Personalized apps have significant care management potential. On the patient level, AI-enabled apps could be specifically tailored to individuals to track progress or adjust treatment plans based on real-time patient feedback.

On an industry level, data generated from users may have the potential to reduce costs on research and development, and improve the accuracy of clinical trials.

7. Follow-ups and Remote Monitoring

Virtual nurse apps can help patients stay accountable by consistently monitoring their own progress. This empowers patients by putting the control in their own hands.

This shift in power is already happening—for instance, a recent survey by Deloitte found that more than a third of respondents are willing to use at-home diagnostics, and more than half are comfortable telling their doctor when they disagree with them.

It’s All About the Experience

Through the use of wearables, smart devices, and personalized apps, patients are becoming increasingly more connected, and therefore less dependent on traditional healthcare.

However, as virtual care becomes more common, healthcare workers need to maintain a high quality of care. To do this, virtual training for physicians is critical, along with user-friendly platforms and intentionally designed apps to provide a seamless user experience.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.visualcapitalist.com/7-ways-artificial-intelligence-is-improving-healthcare/

Continue Reading
PR Newswire2 days ago

Polystyrene Foam Market worth $32.2 billion by 2026 – Exclusive Report by MarketsandMarkets™

Blockchain5 days ago

The Reason for Ethereum’s Recent Rally to ATH According to Changpeng Zhao

Blockchain4 days ago

Chiliz Price Prediction 2021-2025: $1.76 By the End of 2025

Blockchain5 days ago

Mining Bitcoin: How to Mine Bitcoin

Blockchain5 days ago

Mining Bitcoin: How to Mine Bitcoin

PR Newswire4 days ago

Teamsters Lead Historic Defeat of CEO Pay at Marathon Petroleum

Blockchain5 days ago

Amid XRP lawsuit, Ripple appoints former US Treasurer to its board, and names new CFO

Esports5 days ago

TFT 11.9 B-patch nerfs Mordekaiser and LeBlanc

Blockchain5 days ago

Mining Bitcoin: How to Mine Bitcoin

AR/VR3 days ago

Apple is giving a laser company that builds some of its AR tech $410 million

Blockchain4 days ago

Galaxy Digital Set To Buy BitGo for $1.2 Billion

Blockchain5 days ago

‘DeFi may lead to a paradigm shift’ says Federal Reserve Bank paper

Blockchain2 days ago

Launch of Crypto Trading Team by Goldman Sachs

Esports5 days ago

Umbreon VMAX, Glaceon VMAX, and Eeveelution Trainer items revealed for Pokémon OCG set Eevee Heroes

Automotive4 days ago

Brembo Debuts Light-Up LED Brake Calipers

Aviation1 day ago

What Happened To Lufthansa’s Boeing 707 Aircraft?

Esports5 days ago

When does Destiny 2 Season of the Splicer start and end?

Private Equity4 days ago

Beyond the fanfare and SEC warnings, SPACs are here to stay

Payments3 days ago

G20 TechSprint Initiative invites firm to tackle green finance

Blockchain5 days ago

New York bill proposes to ban crypto mining for 3 years over carbon concerns

Trending