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Providing a Clear Competitive Edge to Financial Institutions

By Mario Gamboa - Intelimétrica
CEO

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By Mario Gamboa | CEO & founder - Thu, 01/12/2023 - 10:00

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Every successful business process automation is predominantly centered on data analytics. The field continues to be one of the fastest-growing industries and its need for specialized professionals is only increasing by the day. Analytic Insights forecasts the process automation market will grow from US$95 billion in 2021 to US$323 billion in 2026 worldwide, an annual increase of almost 28 percent.

Technological adoption of components, such as artificial intelligence, machine learning, and the internet of things (IoT), has fueled much of this growth. Data experts are now a key driver of companies' growth due to the exponential increase in the volume and complexity of the information collected. As enterprises move toward digitalization, leveraging data to deliver key business insights will help companies deal with new challenges.

The global data science market has developed a number of applications, such as anomaly detection, pattern recognition, sentiment analysis, and predictive modeling, that have significantly improved digital transformation in lending.

The finance community has been remarkably open to adopting these technologies. Based on predictive risk models, for example, artificial intelligence can help financial institutions identify the best business projects, personal loans, or mortgages to finance. Credit risk is a constant challenge for banks and non-bank lenders and, in times of high inflation, it becomes an even more relevant threat. 

Fortunately, digital mortgage originations are making it possible to take control of such risks throughout the entire loan life cycle. The curation of simple and repetitive tasks designed to curate customer data quality and the digitization and pre-filling of application forms are examples of these simple innovations.

To achieve exponential growth and profitability, technology must focus on processes and features that can be controlled, from cost management to risk evaluation. Data analytics, for example, should be centered on default cues and customer learning to identify how to increase margins, improve risk management, and accelerate turnaround times.

Large credit organizations typically experience slow technology adoption driven by complexities in their operations and outdated legacy systems. In spite of these challenges, these institutions can respond quickly and effectively to customers' demands.

In Mexico, for example, a major multinational bank actively engaged in the mortgage loan space found that certain stages of their underwriting process took an unusually long time to complete, thereby creating bottlenecks and causing poor conversion rates. Delays led to customers' discontent and lost market participation.

Like most banks, the institution produced large volumes of data, but wasn't clear on what stages of the loan process caused detraction. This is not surprising, as many of these steps are supervised and performed by different areas of the bank, under siloed legacy systems and subject to manual processes.

Although the organization was willing to make all information available centrally, it was not accessible to all divisions in real-time. To do this correctly, it turned to Intelimétrica.

The firm created a digital platform called SHIP, a mortgage loan underwriting and control software that provides end-to-end visibility of process completion and bottleneck identification, generating significant savings in both time and cost.

Constant analysis of the data generated by the underwriting process enabled precise measurement for each mortgage broker, the first stage of customer acquisition. The bank gained immediate insight and is now able to prevent or solve backlogs that occur at this stage. The banks can also provide continuous feedback to brokers and reward those that contribute the most to the area's productivity.

Specifically, SHIP empowers mortgage loan officers to: 

  • Review mortgage loan status in real time, from any off-site location 

  • Track performance and advance applications faster, through the interactive use of dashboards and control views

  • View and act on mortgage applications delays

  • Browse searches per location, loan type, phase, branch, staff person, and broker

  • Create and download custom performance reports

Through SHIP, the bank's mortgage management team is able to: 

● View the broker-dealer rankings within a given time frame, identifying executives who fall further than average in their category and their corresponding number of applications in a given time range 

● Identify brokers/sales executives who are delayed more than average in their category and the number of corresponding applications in a given time range.

● Identify which broker/sales representatives are causing a higher amount of reprocesses and at which phase of the credit approval process

● Generate and download automated reports

Following SHIP implementation, and with no added marketing efforts, this bank recovered 20 percent of already qualifying customers previously lost by not exploiting its data in a smart fashion. With the support of Intelimétrica, the processes were fine-tuned and the bottlenecks were unlocked, providing a clear competitive edge to the bank. 

Data analytics and technologies allow all types of financial institutions to efficiently tackle market challenges. The focus of these technologies must be on empowering customers and employees' and leveraging the organization’s talent pool to streamline processes and advance its digital agenda.

Photo by:   Mario Gamboa

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