From underwriting, to originating, to risk monitoring, every step of the commercial lending process is highly dependent upon data. Increasingly, commercial lenders are realizing that their ability to effectively manage data is key to achieving revenue goals.
Digital lending technology plays an essential role in optimizing data management processes. Automated, AI-driven solutions are able to synthesize, parse, and draw insights from large volumes of data all while keeping organizational overhead at a minimum.
The benefits of automated loan management solutions are tremendous, and value is added to every stage of the loan lifecycle. This blog post will explore how cutting-edge SaaS lending solutions like AXIS by AIO Logic can automate data management workflows in commercial lending.
The Current Challenges of Data Management Among Lenders
Lenders today heavily rely on manual, outdated loan management processes that result in error-laden data and the need for extensive manual labor to organize data and glean meaningful insights.
Let’s take a look at the pitfalls of modern data management processes among commercial lenders. With this groundwork laid, the significance of digital lending solutions will become that much clearer.
Challenge #1: Data Fragmentation
Data fragmentation is a massive problem for lenders. Many lenders are dealing with disparate data points spread out across various platforms — such as spreadsheets, multiple software platforms, and memos.
Additionally, this data is often stored in various forms. Some data is conditional, and some is dynamic. Some is qualitative, and some is quantitative. And even in instances where all the data is quantitative, it likely needs to be common-sized so that every data point is measured by the same metric.
Challenge #2: Excess Manual Labor
Messy and ineffective data management naturally leads to extensive manual labor. Underwriting teams have to sort through scattered documents — some digital and some paper-based — in order to manually aggregate data, crunch numbers, create necessary documents, and perform ongoing risk monitoring.
Clearly, this process does not foster scalability. Lenders that heavily rely on manual data management will ultimately struggle to scale their portfolios.
Challenge #3: Inaccurate — and Meaningless — Data
Manual data entry and analysis greatly increase the odds of errors and inconsistencies. The repercussions of error-laden data are many: risk issues, time wasted on resolving discrepancies, and an inability to derive accurate insights.
How Lenders Can Enhance Data Management Processes
Lenders can streamline data management by utilizing powerful loan management systems that employ machine learning to automate key processes.
Traditional data management workflows are causing commercial lenders to leave money on the table. According to Deloitte, between 30 and 40 percent of lending resources’ time is spent on manually managing basic tasks and disparate systems that are capable of being automated.
Especially as digital transformation ramps up, those that stick with legacy systems and processes will lose their standing with borrowers and find it a challenge to increase loan capacity.
Data Automation Throughout the Commercial Lending Lifecycle
Let’s run through every step of the loan lifecycle and explore how each can be optimized with AI-powered loan management solutions like AXIS by AIO Logic.
1. Data Acquisition: Gathering all necessary documentation and data points
Traditional approach: The borrower shares their financials via email or by uploading them to a cloud drive.
Automated approach: Artificial intelligence automatically determines the required documentation, and API integrations pull all the necessary financials — which are then uploaded to the lender’s secure and centralized financial warehouse.
2. Data Organization: Standardizing and streamlining data
Traditional approach: Data is maintained in multiple places and formats. As a result, it has to be manually entered into a single platform, spread, and tediously standardized and common-sized so that it fits into a repeatable framework.
Automated approach: Spreading is automated with one-time mapping of borrower line items and conversion to common size. With the help of rule-based logic and machine learning, data is compiled into the desired format and then imported into one centralized location.
3. Data Analysis: Applying business logic and deriving meaningful insights
Traditional approach: The analyst has to manually perform calculations and apply financial ratios. Oftentimes, business logic is conditional and changes on a case-by-case basis, which makes for an even more time-consuming process.
Automated approach: Data calculations and analysis are performed automatically. This includes horizontal analysis, vertical analysis, ratio analysis, and trend analysis.
4. Ongoing Risk Monitoring: Ensuring that the borrower is in good financial health
Traditional approach: The analyst continuously monitors the borrower’s financial health by closely examining financial statements and verifying that the borrower is complying with loan covenants.
Automated approach: Borrower financial health and portfolio risk are automatically tracked via ongoing monitoring of key metrics — such as revenue and profitability trends, liquidity, and cash flow. If it appears that the borrower’s financial health is deteriorating, then stakeholders are alerted so that steps can be taken to minimize losses.
Boost Efficiencies and Revenue With AXIS by AIO Logic
Commercial lenders have a lot to gain by embracing the next generation of digital lending technology — and even more to lose by sticking with legacy systems. AXIS by AIO Logic allows lenders to centralize data management and tap into a single source of truth. As a result, lenders can benefit from:
- Faster underwriting timeframes
- Reduced overhead costs
- Enhanced operational efficiency
- Greater visibility into risks
- Improved tracking and reporting capabilities
Interested in seeing AXIS’s loan management capabilities firsthand? Contact us for a demo!