Powering the Complete Lending Value Chain with AI
Mortgage lending is a data-intensive business. The volume of available data grows drastically in a mortgage company, with more added every day from calls and payment systems. Needless to say, the success of any lender is built on thoroughly understanding its borrower data, the speed at which it can leverage such data, the degree to which it can meet evolving customer expectations, and the technology it adopts to process it. Challenges faced by lending companies in changing times As the COVID-19 pandemic continues to create changes, many Fintech companies are under stress on many fronts. The pandemic has also exposed modernization needs for critical systems. At the same time, lenders also need to address the challenges such as fluctuating origination volumes, increasing costs, higher expectations from borrowers, and rising competition from new, technology-savvy entrants amid changing times. To compete in this environment — to even expect to stay in business — legacy lenders have started showing their willingness to abandon multiple disparate systems with fragmented data, rigid and inefficient legacy systems & processes, and embrace cutting-edge automation and digitization. How can Fintech fuel innovation amidst changing times? Fintech companies tend to have unique advantages that allow many to create new ways of delivering real value in the current environment and position themselves to thrive in the longer term. Fintech companies have several attributes that give them the agility needed to create and deliver new solutions rapidly. Generally speaking, they are Adept at analyzing and harnessing various types of data, such as credit and underwriting data Exceptionally focused on a seamless and delightful digital customer experience Unlocking the real potential of Artificial Intelligence and Machine Learning to power the complete lending value chain The Fintech space has always been about disruption and driven by innovation, whether it is investments, payments, lending, capital markets, wealth management, or personal finance. From growing revenues, reducing churn, expanding customer bases, or managing risk and efficiencies, AI and machine learning can provide powerful tools for the top fintech companies in the world. Fintech’s traditional tech stacks were not designed to anticipate and act quickly on real-time market indicators and data; they are optimized for transaction speed and scale. What is needed is a new tech stack that can flex and adapt to changing market and customer requirements in real-time. AI and ML have proven to be very powerful at interpreting and recommending actions based on real-time data streams. Machine learning has become ubiquitous, but organizations are struggling to turn data into value. The stakes are high. Those who advance furthest fastest will have a significant competitive advantage; those who fall behind risk becoming irrelevant. It is time for a change Because of rising loan costs, improving operational efficiency has become just as important to lenders as enhancing the borrower experience, maybe even more so. Undoubtedly, why a growing number of lenders have begun embracing artificial intelligence (AI) and machine learning, which remains the two most talked-about next-gen technologies in the mortgage industry today. AI models can help fintech organizations throughout the lifecycle of the loan process. For instance, in the initial phase of the loan process, AI can automate and optimize processes around identifying new target customers, predicting propensity to convert, risk-based pricing. And further, along the lifecycle, AI and ML can bring efficiencies and speed in loan processing through more accurate risk models, detection of fraud, and assisting underwriters with decisioning, managing customer churn, default prediction. These reduce costs, improve processing times, and customer experience. The light at the end of the tunnel The current uncertainty has undeniably placed businesses across the globe under economic duress, and Fintech is no exception. Albeit, many companies in the mortgage arena are already rising to the challenge and arranging their products and services to keep up with the evolving needs of customers who are struggling through the pandemic themselves. What is more, given their differentiated capabilities—namely innovation, resilience, and adaptability— many Fintech companies are well-positioned to survive the crisis and contribute to the industry in meaningful ways once the crisis is behind us. For this unprecedented crisis, if history provides any lessons, it may be that adversity inspires creativity. Final Thoughts Maintaining operational resilience is top of mind of most mortgage companies. Lenders that capitalize on next-gen technology to re-imagine their credit risk scoring and decision systems can enhance the quality of leads and make better recommendations while cutting down manual activities, maintenance costs, and losses. Transform Decision Making Tavant solutions enable customers to make better business decisions every day by incorporating the latest developments in machine learning. To learn more about Tavant’s machine learning-based conditions management & decisioning platform, visit here or reach out to us at [email protected]. FAQs – Tavant Solutions How does Tavant implement AI across the complete lending value chain?Tavant integrates AI throughout every stage of the lending process, from loan origination and underwriting to servicing and collections. Their platform uses machine learning to automate decision-making, reduce processing times, and improve accuracy in credit assessment, document verification, and risk management. What specific AI capabilities does Tavant offer for lending value chain optimization?They provide intelligent document processing, predictive analytics for risk, automated underwriting engines, real-time fraud detection, NLP for customer interactions, and machine learning models that improve decisions based on historical patterns. What is AI in the lending value chain?AI in the lending value chain refers to applying artificial intelligence to all stages of lending, including origination, underwriting, processing, servicing, and collections, to automate processes, improve decision-making, and provide predictive insights. How does AI improve lending efficiency?AI automates repetitive tasks, reduces document review time, speeds credit decisions, minimizes errors, offers 24/7 processing, streamlines compliance checks, and provides predictive analytics for risk. What are the benefits of end-to-end AI lending solutions?Benefits include reduced costs, faster processing, better customer experience, enhanced risk accuracy, improved compliance, and scalability without proportional staff increases.
Driving Efficiency with MLOps & Microsoft Azure
The advancements in machine learning has more and more enterprises turning towards the insights provided by it. Data scientists are busy creating and fine-tuning machine learning models for tasks ranging from recommending music to detecting fraud. However, as is always the case with new technology, machine learning comes with its own set of challenges: Concept Drift – Accuracy of model degrades over time due to disparity in training data vs production data Locality – Pre-trained models’ accuracy levels change with changing demography/geography/customer Data Quality – Changes in data quality affect accuracy levels Scalability – Data scientists, while good at creating models, don’t necessarily have the skills to operationalize models at enterprise scale Process & Collaboration – A lot of models are developed and remain confined to sandboxes or within silos in an organization with no clearly defined process for the model lifecycle Model Governance – Most data science projects have no model governance – who can create models, who can deploy them, what datasets were used for training? Most ML projects do not have this defined clearly A typical ML model lifecycle Here’s what a machine learning model lifecycle looks like: What is MLOps According to Wikipedia, “MLOps (‘Machine Learning’ + ‘Operations’) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production ML lifecycle. MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire lifecycle – from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.” How is MLOps different from DevOps So, is MLOps just another fancy name for DevOps? Since machine learning is also a software system, most of the DevOps practices apply to MLOps too. However, there are some important differences: Team skills: a machine learning team usually has Data Scientists or/and ML researchers who may be excellent with different modeling techniques and algorithms but lack the right software engineering skills for building enterprise-grade production systems. Continuous Integration (CI) is not only about testing and validating code and components, but also testing and validating data, data schemas, and models. Continuous Deployment (CD) goes beyond deploying a package or service. It requires deploying an ML training pipeline that should automatically deploy another service (model prediction service). Continuous Testing is unique to ML systems, which is concerned with automatically retraining and serving the models. Monitoring – ML uses non-intuitive mathematical functions. It requires constant monitoring to ensure its operating within regulation and that the models are making accurate predictions. Implementing MLOps with Azure Machine Learning Tavant’s Manufacturing Analytics Platform (TMAP) is an analytics and machine learning-based platform that provides important business insights to our customers in the manufacturing domain especially Warranty. It is based on Azure and we use Azure Machine Learning’s MLOps features for managing our models’ lifecycle. Here’s a list of features that Azure provides for MLOps: Workspace – An Azure Machine Learning workspace is the foundational resource that is used to experiment, train and deploy machine learning models Development Environment – Azure ML provides multiple pre-configured ML specific VMs and computes instances. These come with most of the Machine Learning and Deep Learning libraries pre-installed and configured. One can also choose to create a local development environment if required Data Set – This step involves connecting to different data sources like Azure Blob Storage, Azure Data Lake, etc. and create a Machine Learning Data set. This Dataset can be used to access the data and its metadata when we create a Run Experiment & Runs – An experiment is a logical grouping of all the trials or runs. For each ‘Run’, you can log the metrics, images, data or enable logging. All these will be attached to the corresponding ‘Run’ under the Experiment. Compute Target – Creating a compute target helps you run your machine learning training. This compute target can be local or remote Azure GPU/CPU VMs Model Training – Azure ML already comes with multiple Estimators for Sklearn, Pytorch, Tensorflow and Keras. These Estimators helps you organize the ML training. Azure ML also has a capability to create Custom Estimators of your choice. All training logs, versions, and details will be logged in under the ‘Run’ of your experiment. Model Registry – Once the ML training is complete with different ‘Runs’ and you get the ‘best model’, the next step is to register the model in the Azure Model Registry. Model Registry maintains the model versions, descriptions of the model, model metadata, etc. Model Profiler – Before you deploy the model for real-time inference, profiling the infrastructure requirements for the model is very important. Profiling will give you a better understanding of how much minimum memory and CPU’s required for the model to give low latency and high throughput. Model Deployment – A model can be deployed to Azure Container Instances or Azure Kubernetes. This step involves providing which model and what version to deploy, its configurations, and the deployment configurations. Data Collection – It is used to capture real-time inputs and predictions from a model. It is used to analyze the performance of a model. Data Drift – It helps you understand the change in features, data quality issues, natural data shift, change in the relationship between features, etc. Conclusion MLOps is a must for enterprises using machine learning at scale. It allows for managing the complete model lifecycle including model governance and should be made mandatory for all Machine Learning projects. Azure Machine Learning provides has a great feature set for implementing MLOps. It does lack some of the advanced features like model lineage but one can always use dedicated MLOps platforms like MLflow or DotScience on Azure to bring in any missing features.