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The Insurance Industry

The insurance industry is notorious for having been a slow adopter of advancing technology in the past. In recent years, however, advancing technology and the sheer amount of data required in claims processing and other processes have force the sector to adopt the use of AI to stay stay ahead of the competition. A recent study from Tata Consultancy Services reported that the insurance sector has invested over $124 million in AI, trumping the $70 million invested in other industries.

Nevertheless, insurers face a number of challenges in their quest to claim and retain customers. Large-scale mistrust, fierce competition and an increasingly demanding consumer base are forcing the industry to evolve quicker than ever. If they want to survive, they are turning to AI to help streamline their workflows, decrease fraud and increase customer-centricity.

The insurance industry is facing more challenges than ever

Consumer expectations are changing. Millenials are demanding a more hassle-free and customer-centric shopping experience.

In a sector suffering slow growth and fierce competition, firms are being forced to work smarter.

Lack of trust burdens the industry, causing many to view insurance as an unnecessary expense.

Product overview

The claims management process is time-consuming and data-intensive including various types of documents such as claims forms, incident forms, medical reports and repair estimates, all with data in both structured and unstructured formats. This data is essential to evaluate the validity of a claim, to ensure against fraud, and to route it through all of the decision making processes.

Our claims management system aids insurers and underwriters speed up the process of evaluation by extracting essential data in all types of documents and structuring it to be fed into the database automatically. This drastically reduces time spent on data-checking, allowing insurers to spend more time focusing on customer-centricity efforts.

The claims management system is a modular solution that allows you to leverage AI capabilities in where needed, from a single step in the mortgage assessment process to across the entire workflow.

Data extraction

Our extraction AI models can be customized to know what to look for in incident forms, medical reports, repair estimates, etc.

Claims processing

Classification models classify claims documents rapidly, cross-checks data for errors and identifies missing or incomplete documentation.

Fraud detection

AI models can learn and discover new cases in new scenarios, automatically evaluating damages and predicting the costs from historical data to prevent fraud.

Success story

Reducing costs, saving time, and increasing accuracy in the life insurance claiming process to improve customer centricity

Problem

A life insurance company offers an annuity-based product in which upon the death of the insured, relatives are able to make their claims by supplying the death certificate and personal identification through the post or email. These documents are scanned and the processing team enters the details manually into the system. The printed forms are then posted to the relatives who sign and send the forms back for the claimed amount to be released.

The process is time-consuming, labour-intensive and potentially riddled with mistakes made during the data-entry process, resulting in the delay of the release of funds. This process not only results in a loss of time, resources and costs for the insurer, but adds additional emotional burden on the relatives during an already emotionally difficult time.

Result

This problem is well-suited to the capabilities of Nexus FrontierTech’s bespoke AI solutions services, in this case a tailor-made Cognitive Document Reader (CDR). Whether the relatives send the death certificate and personal ID in the form of scanned versions or photocopies by post, the CDR can extract all the required information and place it in the database automatically. The processing team would simply double-check the details before releasing the claim.

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The Banking Industry

The hype around the use of AI in the banking industry is undeniable, and for good reason. According to Autonomous Next, AI applications could save banks an estimated $447 billion by 2023. In fact, it’s safe to say that a majority of banks across the board see the benefits this technology could bring to its workflows and already are or planning on implementing AI strategies to stay competitive.

However, with an increasingly competitive landscape, highly-demanding customers and the rise of fraud in the mortgage application process, players in the banking sector are being forced to reevaluate their current system processes and find ways to cut down processing time, protect their client’s data, and distinguish themselves in this highly-competitive market.

The lending market is facing more challenges than ever

Digital-native customers like millenials are expecting lending processes to be at the same level of speed as a click

Fierce competition from agile fintech organizations are forcing lenders to move quicker

Incidents of fraud in mortgage applications are on the rise

Product overview

It’s obvious that mortgage lending is a data-intensive business. The data come from various forms of documents from official forms to. The mortgage lenders need to assimilate, sort, evaluate and weigh all that information to be sure the loan will be repaid.

Our mortgage assessment system helps lenders speed up ingestion of loan applications along with data extraction, loan docs processing and risk assessment support. Lending professionals then can shorten the time for mortgage applications, and keep an eye for fraud to protect both the lenders and the borrowers.

The mortgage assessment system is a modular solution that allows you to leverage AI capabilities in where needed, from a single step in the mortgage assessment process to across the entire workflow.

Data extraction

Our extraction AI models can be customized to know what to look for in W2s, pay stubs, banks statements, and the other loan documents

Loan processing

Classification models classify loan documents rapidly, cross-check data for auto-denials, identify missing or incomplete documentation

Risk assessment

Improve ability to assess loan application by comparing loan documents, identify anomalies and supporting decision making

Interest recommendation

Generate interest recommendations based on loan doc characteristics, support closing disclosure creation

Success story

Automizing the sales quality process in a large global bank

Problem

A large global bank’s UK sales quality (SQ) team reviewed the sale of financial products to ensure regulatory compliance. Only 10%-15% of sales were actually checked, with a group of over 120 reviewers manually reviewing 10+ data sources each. Each review took roughly 90 minutes to complete.

This labour-intensive process left the bank with a higher level of compliance risk, resulting in various faults and errors in unchecked cases going uncorrected. The accuracy levels were also of concern, as inconsistency and potential oversights were made due to human involvement. Lastly, the entire process was slow, with time-consuming reviews and delayed feedback.

Result

In order to improve the speed of the bank’s review process and exceed the statutory review quota, Nexus proposed to automate this internal process to make checks faster, more comprehensive, and with a higher speed and accuracy by checking 100% of samples and conducting verifications in real-time using AI models to reach near 100% accuracy levels. All this enabled the elimination of the backlog of checks, ultimately moving them to the point of sale.

Nexus successfully helped the global bank standardise and reduce the time spent in their internal sales quality process, check 100% of sales with near-perfect accuracy, free-up their employees’ capacity, and improve customer service

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