Banking

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

See PDF Case Study

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