Vova Pylypchatin’s Post

Events are an integral part of operational mortgage data. Here's what event data means 👇 🔍 Definition: Event data refers to facts describing occurrences of an event or action at a specific time. This data type records the interactions, transactions, or events over time. 🧬 Characteristics: ↳ Time-based: Event data is inherently time-based, describing when an event occurred. ↳ Action-oriented: In operations, event data is action-oriented and usually represents a completed task within a specific process. ↳ Continuous: Event data is generated continuously as new events occur, leading to a dynamic dataset that grows and changes over time. ↳ Immutable: Events are immutable by nature, meaning that once an event occurs, nothing about it changes. 📊 Examples: ↳ Application taken ↳ Loan estimate sent ↳ Loan taken into processing ↳ Loan status changed from "under review" to "approved." ↳ Loan funded 🛠️ Facts that describe an event: An event can be characterized by the following facts: ↳ Type of the Event: What happened ↳ Time of the Event: When it happened ↳ Context of the Event: Who initiated or performed the action, and who or what was affected ↳ Properties of the Event: Characteristics unique to this particular event 🎯 Purpose: Event data is usually used for: ↳ Analyzing behaviors, patterns, or trends over time ↳ Understanding changes in entity data over time ↳ Performing actions in response to specific events #mortgage #mortgagetech #lendtech #fintech

Operational data gap limits mortgage companies’ automation potential. Here’s a solution that helps to close the gap 👇 🔁 Operational data pipelines An operational data gap is the difference between the operational data produced and the operational data ready for software processing. 👉 An operational data pipeline is a series of operations that automate raw operational data collection, storage, and transformation into a format ready for software processing. The operational data pipeline systematically extracts raw data, transforms it, and loads it into storage where operational software can access it. The smaller the gap, the more operational data the software can process. The more data available for software processing, the higher the potential for automation and analytics. #mortgage #mortgagetech #lendtech #fintech

“How do you set up Operational Mortgage Analytics?” Here’s my 5-step process 👇 1️⃣ Document questions you want to answer The primary function of any analytics is to answer questions. So, analytics setup is defined by what questions you want to answer. Start by making a list of the questions you want to get answered with your operational analytics. 2️⃣ Document what reports will answer your questions Reports are the way you get answers through analytics. List what reports you’ll need to answer the questions you need. Reports primarily defined by: ↳ What data to display ↳ How to display data 3️⃣ Document data required to build these reports Reports need data to work. No data, no reports. Document what data you need to collect and in what format to power your reports. Data usually comes down to: ↳ What events do you need to track ↳ What data each event should have ↳ What entities do you need to have ↳ What data each entity should have 4️⃣ Collect the data for the reports Once you know what data you need, the next step is collecting it. Usually, the data collection process involves: ↳ Data extraction from sources (LOS, CRM, etc) ↳ Cleaning and unifying the data ↳ Creating more data (e.g., generate events based on change data capture) 5️⃣ Set up reports based on the data The last step is to set up the reports you defined in Step 2 using the data collected in Step 4. You can use any BI or data visualization software to set up reports. #mortgage #mortgagetech #lendtech #fintech

"How has the loan processing team's performance changed over time?" Here's a 5-step process to answer this using operational analytics 👇 0️⃣ Get operational data (Pre-requisite) To answer such questions, it's essential to have operational data collected for analytics. The steps below rely on having the necessary operational data. 1️⃣ Filter First, identify which event represents performance and narrow it down. For a loan processor, this might be 'Loan Closed,' so filter out only these events. 2️⃣ Aggregate To understand performance, we need to know the number of loans closed. Use aggregation to calculate the total count of closed loans. 3️⃣ Group The total count gives an insight into how many loans were processed by the team but doesn't reflect individual performance. To understand how many loans each loan processor closed, group the aggregation results by loan processors. 4️⃣ Turn into time series After grouping, you'll know how many loans each loan processor closed over a period. However, this doesn't show how the count changed over time. To see performance trends, update your query to get a time series aggregation. 5️⃣ Visualize The final step is to turn raw data into a visualization that answers the question. In this case, a line chart is the best option. #mortgage #mortgagetech #lendtech #fintech

Coming from a tech background, I love when technology enhances efficiency and streamlines tedious processes. At Loan Market, we’ve implemented a feature called ‘SmartData.’ Normal Process: - Email me your photo ID - Email me your bank statements - Tell me about your living expenses These initial steps require considerable time and involve a lot of back and forth, and we are just getting started. SmartData Process: - Captures ID and contact information - Allows you to securely connect to your bank accounts, fetch data, and populate your categorised expenses in the fact find form ready for analysis - Runs a credit check and populates your existing liabilities (such as existing loans and their limits) in the fact find form, ensuring no existing liability is overlooked SmartData fast-tracks your loan application and streamlines the entire process. Smart. Innit? #homeloans #mortgage #firsthome #smsf #refinance #loanmarket #smartdata

“How to use Operational Analytics for due date tracking?” Calculate and capture due dates based on real-time loan activity 👇 With operational analytics, you can automatically calculate due dates and capture them based on the changes in your LOS or other system. It relies on automated future activity capture, where, based on the real-time loan activity, you generate new activities that should happen. For example, you can capture due dates (future activities) based on the real-time loan activity like: ↳ Application Taken → Loan decision ↳ Rate Locked → Loan estimate redisclosure ↳ Offer Accepted → Loan Commitment And then automate the calculation of their due date based on the: ↳ Triggering activity date (e.g., a date when an offer was accepted) ↳ Activity and loan properties (e.g., financing contingency days) ↳ Day of the week due dates fall on (e.g., whether it’s a holiday or not) Automated future activity capture can reliably generate a feed of the due dates that you can later use for reporting and automation. #mortgage #mortgagetech #lendtech #fintech

Operational analytics is great for tracking mortgage company performance. Here are 5 reasons why 👇 ⚙️ Automated No need to manually enter data or wrangle spreadsheets to generate a report. Data is automatically collected and fed into reports. ⏰ Real-time Data is collected and delivered in real time to ensure that reports are always up-to-date with the latest changes. ✅ Accurate Automated data collection and visualization minimize the room for human error. 📈 Historical Event-based architecture allows performance tracking over time, providing insights to identify trends and make forecasts. 🔎 Detailed Rich operational data offers more granularity. Managers and executives can zoom in and out to the level of detail they need. #mortgage #mortgagetech #lendtech #fintech

Operational data is the foundation for mortgage automation. Here’s how they work together 👇 Operational automation relies on 2 types of operational data: ↳ Activity data → events in the operations (e.g., application taken, rate locked). ↳ Entity data → subjects of the event (e.g., loan application, borrower). Automation uses real-time activity to: ↳ Trigger automated workflow execution ↳ Transfer data between automated workflows And it uses structured data to perform actions. Data is the foundation of automation. Without operational data, there is no operational automation. Thus, most automation projects start by first collecting the required operational data. #mortgage #mortgagetech #lendtech #fintech

Mortgage analytics and automation rely on operational data. But what makes data operational? Here’s my analysis 👇 If Data is unprocessed facts about the world. Then, Operational data is unprocessed facts about day-to-day business operations. This Data includes customer interactions, actions within processes, and external facts brought into the business from customers, employees, etc. Two types of facts can characterize facts Operational data: ↳ Facts about events → Event data ↳ Facts about entities → Entity data Event data refers to facts describing occurrences of an event or action at a specific time. This data type records the interactions, transactions, or events over time. Entity data refers to facts that describe the properties of objects, people, or concepts existing within business operations over time. Together, event and entity data compose the bulk of operational data, essential for businesses to carry out their core functions. #mortgage #mortgagetech #sixsigma #operations

“How does data warehouse apply to mortgage operations?” Here’s how it can help lenders drive operational efficiency 👇 Lenders can drive operational efficiency by leveraging automation and analytics. However, both automation and analytics potential are limited by operational data gaps. ℹ️ An operational data gap is the difference between operational data produced and operational data ready for software processing. 👉 Data warehouse plays an integral role in closing the data gap. A data warehouse is designed to store high volumes of structured data. However, the fact that storage is performant doesn’t make it a data warehouse; many data stores are. The data stored within the storage is what makes it a data warehouse. 🏦 Data warehouse is a centralized storage for the processed operational data of the company. From that storage, companies can use operational data to: ↳ Automate mortgage metrics tracking ↳ Automate mortgage workflow execution ↳ Sync data across all operational tools ↳ Power custom internal tools Thus helping lenders increase operational efficiency. #mortgage #mortgagetech #lendtech #fintech