Vova Pylypchatin’s Post

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

Operational Automation is closely intertwined with Operational Analytics. Here are 2 ways how analytics enables Automation: 👇 🔍 Identify processes ripe for Automation Operational data often informs Automation. By examining operational data, you can pinpoint inefficiencies or errors within the processes. 📊 Evaluate the impact of Automation After implementing Automation, operational analytics can be used to understand its effect on the performance of operations. Analytics and Automation go hand in hand in the improvement of processes. #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

Operational data is the foundation for mortgage automation. Here’s how they work together 👇 Operational automation relies on 2 types of operational data: ↳ Activity → events in the operations (e.g., application taken, rate locked). ↳ Entities → context 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 entities 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

How do you use operational analytics in mortgage operations? Here are 4 practical use cases 👇 ⚙️ Process Optimization and Automation By analyzing operational data, you can pinpoint problems and inefficiencies in the processes. After implementing changes, analytics can be used to assess the effectiveness of these changes. 🚣 Team Performance Monitoring and Accountability Operational analytics can quantify team and individual performance through measurable metrics. Managers can objectively assess performance by analyzing data such as the number of loans processed, underwriting accuracy, and customer interactions. 📅 Due Date Tracking You can automate due date calculation and capture them as events in the future using real-time loan events as triggers. Then, you can use these future events to create reports that provide a quick overview of each loan's status in the pipeline and any required actions. 📊 Company Performance Monitoring and Forecasting Managers and executives can gauge the overall company standing by using operational data to create reports on key performance indicators (KPIs). Thanks to the event-based nature of operational analytics, it's possible to retrieve current performance and analyze how it has changed over time. This performance data can provide insights needed to identify trends and build forecasts. #mortgage #mortgagetech #lendtech #fintech

Our company began because we saw a lack of certain business intelligence tools and data models in the market. Listen to our CEO, Tim Armbruster, quickly explain how this was why Grind Analytics was born – to provide business intelligence to the mortgage industry. Listen to Tim here and visit https://lnkd.in/eMSBCwX4 for more information. #business #businessintelligence #grindanalytics #data #datacollection #dataanalytics #machinelearning #artificialintelligence #operations #businessoperations #CRM #clients #technology #IT #growth #AI #solutions #Csuite #Fortune500 #tech #executives #decisionmaking #development #developers #consulting #mortgage #lending #insurance #applications #datasecurity #riskmanagement 

🚀 We are thrilled to announce our strategic partnership with Total Expert, a leader in customer engagement platforms for modern financial institutions. This collaboration marks a significant step in enhancing our Empower Loan Origination System (LOS) with the best-in-class automation and customer engagement solutions provided by Total Expert. 🚀 🤝 Our integration aims to redefine the mortgage lending landscape by boosting lender revenue, improving sales productivity, and closing more loans through enhanced lead generation and customer interaction. This is the future of mortgage tech—empowering lenders with tools that streamline the origination process and personalize the homebuyer journey. 🌐 Read all about how this partnership will transform mortgage lending and create new opportunities for growth and efficiency. Don't miss out on the details of this game-changing alliance! 🔗 https://lnkd.in/eUaTumsE #DarkMatterTechnologies #TotalExpert #MortgageTechnology #Innovation #Partnership #BusinessGrowth #FinTech #Horizon2024

“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

“Events” are the cornerstone of Operational Mortgage Analytics. Here’s what it means 👇 An event is defined as something that occurred at a particular point in time. Usually, it represents an action performed on/by an object or person. In operational mortgage analytics, an event is usually an action within a specific process. Here are some examples of the events: ↳ Application taken ↳ Loan estimate sent ↳ Loan taken into processing ↳ Appraisal received ↳ Rate locked ↳ Rate lock expired ↳ Financing contingency expired ↳ Loan closed ↳ etc. Here’s an example of the event structure: ↳ Id → what’s a unique identifier of the event ↳ Type → what event happened ↳ Date → when the event happened ↳ Loan → what loan was subject to the event ↳ Properties → contextual information about the event Events are ingested into the “pipeline” from the “Event source.” Then, it flows into the “Event destination.” Usually for permanent storage. On the “pipeline” between source and destination may sit “Processors” that can: ↳ Transform events and pipe them back into the pipeline ↳ Transform events and pipe them into different pipeline ↳ React on the events and trigger alerts and automation This events-based architecture enables: ↳ Real-time reporting ↳ Real-time automation and alerts ↳ Value over time reports ↳ Conversion rate reports ↳ Time between events reports #mortgage #mortgagetech #lendtech #fintech

Next up in our feature of Mortgage Tech Day presenters is Conductiv! "Conductiv increases approval rates by providing missing data and AI. ➡️ Faster manual approvals - Save time and effort by gathering income, identity, and other stipulation data digitally and approve loans 2-5x faster. ➡️ More auto approvals - Automate approvals using rules that mimic human behavior and follow underwriting best practices and standards. ➡️ Enriched decisioning - Use first party, permissioned data and machine learning insights to transform lending." Learn more at the link below. See you in Denver! #tmc #mortgage #tech #fintech #techfund #emergingtechfund