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Cognitive document processing for automated mortgage processing

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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.

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Source: https://aws.amazon.com/blogs/machine-learning/cognitive-document-processing-for-automated-mortgage-processing/

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Optimal Dynamics nabs $22M for AI-powered freight logistics

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Optimal Dynamics, a New York-based startup applying AI to shipping logistics, today announced that it closed a $18.4 million round led by Bessemer Venture Partners. Optimal Dynamics says that the funds will be used to more than triple its 25-person team and support engineering efforts, as well as bolster sales and marketing departments.

Last-mile delivery logistics tends to be the most expensive and time-consuming part of the shipping process. According to one estimate, last-mile accounts for 53% of total shipping costs and 41% of total supply chain costs. With the rise of ecommerce in the U.S., retail providers are increasingly focusing on fulfilment and distribution at the lowest cost. Particularly in the construction industry, the pandemic continues to disrupt wholesalers — a 2020 Statista survey found that 73% of buyers and users of freight transportation and logistics services experienced an impact on their operations.

Founded in 2016, Optimal Dynamics offers a platform that taps AI to generate shipment plans likely to be profitable — and on time. The fruit of nearly 40 years of R&D at Princeton, the company’s product generates simulations for freight transportation, enabling logistics companies to answer questions about what equipment they should buy, how many drivers they need, daily dispatching, load acceptance, and more.

Simulating logistics

Roughly 80% of all cargo in the U.S. is transported by the 7.1 million people who drive flatbed trailers, dry vans, and other heavy lifters for the country’s 1.3 million trucking companies. The trucking industry generates $726 billion in revenue annually and is forecast to grow 75% by 2026. Even before the pandemic, last-mile delivery was fast becoming the most profitable part of the supply chain, with research firm Capgemini pegging its share of the pie at 41%.

Optimal Dynamics’ platform can perform strategic, tactical, and real-time freight planning, forecasting shipment events as far as two weeks in advance. CEO Daniel Powell — who cofounded the company with his father, Warren Princeton, a professor of operations research and financial engineering — says that the underlying technology was deployed, tested, and iterated with trucking companies, railroads, and energy companies, along with projects in health, ecommerce, finance, and materials science.

“Use of something called ‘high-dimensional AI’ allows us to take in exponentially greater detail while planning under uncertainty. We also leverage clever methods that allow us to deploy robust AI systems even when we have very little training data, a common issue in the logistics industry,” Powell told VentureBeat via email. “The results are … a dramatic increase in companies’ abilities to plan into the future.”

The global logistics market was worth $10.32 billion in 2017 and is estimated to grow to $12.68 billion USD by 2023, according to Research and Markets. Optimal Dynamics competes with Uber, which offers a logistics service called Uber Freight. San Francisco-based startup KeepTruckin recently secured $149 million to further develop its shipment marketplace. Next Trucking closed a $97 million investment. And Convoy raised $400 million at a $2.75 billion valuation to make freight trucking more efficient.

But 25-employee Optimal Dynamics investor Mike Droesch, a partner at BVP, says that demand remains strong for the company’s products. “Logistics operators need to consider a staggering number of variables, making this an ideal application for a software-as-a-service product that can help operators make more informed decisions by leveraging Optimal Dynamics industry leading technology. We were really impressed with the combination of their deep technology and the commercial impact that Optimal Dynamics is already delivering to their customers,” he said in a statement.

With the latest funding round, a series A, Optimal Dynamics has raised over $22 million to date. Beyond Bessemer, Fusion Fund, The Westly Group, TenOneTen Ventures, Embark Ventures, FitzGate Ventures, and John Larkin and John Hess also contributed .

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Source: https://venturebeat.com/2021/05/13/optimal-dynamics-nabs-22m-for-ai-powered-freight-logistics/

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Code-scanning platform BluBracket nabs $12M for enterprise security

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Code security startup BluBracket today announced it has raised $12 million in a series A round led by Evolution Equity Partners. The capital will be used to further develop BluBracket’s products and grow its sales team.

Detecting exploits in source code can be a pain point for enterprises, especially with the onset of containerization, infrastructure as code, and microservices. According to a recent Flexera report, the number of vulnerabilities remotely exploitable in apps reached more than 13,300 from 249 vendors in 2020. In 2019, Barracuda Networks found that 13% of security pros hadn’t patched their web apps over the past 12 months. And in a 2020 survey from Edgescan, organizations said it took them an average of just over 50 days to address critical vulnerabilities in internet-facing apps.

BluBracket, which was founded in 2019 and is headquartered in Palo Alto, California, scans codebases for secrets and blocks future commits from introducing new risks. The platform can monitor real-time risk scores across codebases, git configurations, infrastructure as code, code copies, and code access and resolve issues, detecting passwords and over 50 different types of tokens, keys, and IDs.

Code-scanning automation

Coralogix estimates that developers create 70 bugs per 1,000 lines of code and that fixing a bug takes 30 times longer than writing a line of code. In the U.S., companies spend $113 billion annually on identifying and fixing product defects.

BluBracket attempts to prevent this by proactively monitoring public repositories with the highest risk factors, generating reports for dev teams. It prioritizes commits based on their risk scores, minimizing duplicates using a tracking hash for every secret. A rules engine reduces false positives and scans for regular expressions, as well as sensitive words. And BluBracket sanitizes commit history both locally and remotely, supporting the exporting of reports via download or email.

BluBracket offers a free product in its Community Edition. Both it and the company’s paid products, Teams and Enterprise, work with GitHub, BitBucket, and Gitlab and offer CI/CD integration with Jenkins, GitHub Actions, and Azure Pipelines.

BluBracket

Above: The Community Edition of BluBracket’s software.

Image Credit: BluBracket

“Since our introduction early last year, the industry has seen through Solar Winds how big of an attack surface code is. Hackers are exploiting credentials and secrets in code, and valuable code is available in the public domain for virtually every company we engage with,” CEO Prakash Linga, who cofounded BluBracket with Ajay Arora, told VentureBeat via email.

BluBracket competes on some fronts with Sourcegraph, a “universal code search” platform that enables developer teams to manage and glean insights from their codebase. It has another rival in Amazon’s CodeGuru, an AI-powered developer tool that provides recommendations for improving code quality. There’s also cloud monitoring platform Datadog, codebase coverage tester Codecov, and feature-piloting solution LaunchDarkly, to name a few.

But BluBracket, which has about 30 employees, says demand for its code security solutions has increased “dramatically” since 2020. Its security products are being used in “dozens” of companies with “thousands” of users, according to Linga.

“DevSecOps and AppSec teams are scrambling, as we all know, to address this growing threat. By enabling their developers to keep these secrets out of code in the first place, our solutions make everyone’s life easier,” Linga continued. “We are excited to work with Evolution on this next stage of our company’s growth.”

Unusual Ventures, Point72 Ventures, SignalFire, and Firebolt Ventures also participated in BluBracket’s latest funding round. The startup had previously raised $6.5 million in a seed round led by Unusual Ventures.

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Source: https://venturebeat.com/2021/05/13/code-scanning-platform-blubracket-nabs-12m-for-enterprise-security/

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Data governance and security startup Cyral raises $26M

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Data security and governance startup Cyral today announced it has raised $26 million, bringing its total to date to $41.1 million. The company plans to put the funds toward expanding its platform and global workforce.

Managing and securing data remains a challenge for enterprises. Just 29% of IT executives give their employees an “A” grade for following procedures to keep files and documents secure, according to Egnyte’s most recent survey. A separate report from KPMG found only 35% of C-suite leaders highly trust their organization’s use of data and analytics, with 92% saying they were concerned about the reputational risk of machine-assisted decisions.

Redwood City, California-based Cyral, which was founded in 2018 by Manav Mital and Srini Vadlamani, uses stateless interception technology to deliver enterprise data governance across platforms, including Amazon S3, Snowflake, Kafka, MongoDB, and Oracle. Cyral monitors activity across popular databases, pipelines, and data warehouses — whether on-premises, hosted, or software-as-service-based. And it traces data flows and requests, sending output logs, traces, and metrics to third-party infrastructure and management dashboards.

Cyral can prevent unauthorized access from users, apps, and tools and provide dynamic attribute-based access control, as well as ephemeral access with “just-enough” privileges. The platform supports both alerting and blocking of disallowed accesses and continuously monitors privileges across clouds, tracking and enforcing just-in-time and just-enough privileges for all users and apps.

Identifying roles and anomalies

Beyond this, Cyral can identify users behind shared roles and service accounts to tag all activity with the actual user identity, enabling policies to be specified against them. And it can perform baselining and anomaly detection, analyzing aggregated activity across data endpoints and generating policies for normal activity, which can be set to alert or block anomalous access.

“Cyral is built on a high-performance stateless interception technology that monitors all data endpoint activity in real time and enables unified visibility, identity federation, and granular access controls. [The platform] automates workflows and enables collaboration between DevOps and Security teams to automate assurance and prevent data leakage,” the spokesperson said.

Cyral

Existing investors, including Redpoint, Costanoa Ventures, A.Capital, and strategic investor Silicon Valley CISO Investments, participated in Cyral’s latest funding round. Since launching in Q2 2020, Cyral — which has 40 employees and occupies a market estimated to be worth $5.7 billion by 2025, according to Markets and Markets — says it has nearly doubled the size of its team and close to quadrupled its valuation.

“This is an emerging market with no entrenched solutions … We’re now working with customers across a variety of industries — finance, health care, insurance, supply chain, technology, and more. They include some of the world’s largest organizations with complex environments and some of the fastest-growing tech companies,” the spokesperson said. “With Cyral, our company was built during the pandemic. We have grown the majority of our company during this time, and it has allowed us to start our company with a remote-first business model.”

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Source: https://venturebeat.com/2021/05/13/data-governance-and-security-startup-cyral-raises-26m/

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AI

Data governance and security startup Cyral raises $26M

Avatar

Published

on

Join Transform 2021 this July 12-16. Register for the AI event of the year.


Data security and governance startup Cyral today announced it has raised $26 million, bringing its total to date to $41.1 million. The company plans to put the funds toward expanding its platform and global workforce.

Managing and securing data remains a challenge for enterprises. Just 29% of IT executives give their employees an “A” grade for following procedures to keep files and documents secure, according to Egnyte’s most recent survey. A separate report from KPMG found only 35% of C-suite leaders highly trust their organization’s use of data and analytics, with 92% saying they were concerned about the reputational risk of machine-assisted decisions.

Redwood City, California-based Cyral, which was founded in 2018 by Manav Mital and Srini Vadlamani, uses stateless interception technology to deliver enterprise data governance across platforms, including Amazon S3, Snowflake, Kafka, MongoDB, and Oracle. Cyral monitors activity across popular databases, pipelines, and data warehouses — whether on-premises, hosted, or software-as-service-based. And it traces data flows and requests, sending output logs, traces, and metrics to third-party infrastructure and management dashboards.

Cyral can prevent unauthorized access from users, apps, and tools and provide dynamic attribute-based access control, as well as ephemeral access with “just-enough” privileges. The platform supports both alerting and blocking of disallowed accesses and continuously monitors privileges across clouds, tracking and enforcing just-in-time and just-enough privileges for all users and apps.

Identifying roles and anomalies

Beyond this, Cyral can identify users behind shared roles and service accounts to tag all activity with the actual user identity, enabling policies to be specified against them. And it can perform baselining and anomaly detection, analyzing aggregated activity across data endpoints and generating policies for normal activity, which can be set to alert or block anomalous access.

“Cyral is built on a high-performance stateless interception technology that monitors all data endpoint activity in real time and enables unified visibility, identity federation, and granular access controls. [The platform] automates workflows and enables collaboration between DevOps and Security teams to automate assurance and prevent data leakage,” the spokesperson said.

Cyral

Existing investors, including Redpoint, Costanoa Ventures, A.Capital, and strategic investor Silicon Valley CISO Investments, participated in Cyral’s latest funding round. Since launching in Q2 2020, Cyral — which has 40 employees and occupies a market estimated to be worth $5.7 billion by 2025, according to Markets and Markets — says it has nearly doubled the size of its team and close to quadrupled its valuation.

“This is an emerging market with no entrenched solutions … We’re now working with customers across a variety of industries — finance, health care, insurance, supply chain, technology, and more. They include some of the world’s largest organizations with complex environments and some of the fastest-growing tech companies,” the spokesperson said. “With Cyral, our company was built during the pandemic. We have grown the majority of our company during this time, and it has allowed us to start our company with a remote-first business model.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://venturebeat.com/2021/05/13/data-governance-and-security-startup-cyral-raises-26m/

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