AI for Lending
Loan Origination
Credit Decisioning Logik
TM
Lender Matching Logik
TM
Statement Data Automation Logik
TM
Bank Statement Visualizer Logik
TM
Sales Marketing
Marketing Logik
TM
Cross-Selling Logik
TM
Borrower Lead Routing Logik
TM
Flexible Funding Logik
TM
Payment & Collections
Bad Debt Analyzer Logik
TM
Compliance
Salesforce for Lending
Advisory
Implementation
Development
Managed Services
App Exchange Development
Salesforce Integrations
Remote Salesforce Team
Salesforce Expertise
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Sales Cloud
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Marketing Cloud & Pardot
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Powerful
AI-driven Solutions
Built Around Your
Lending Business
AI Capabilities
Designed Around Your
Lending Needs
Loan origination
Credit Decisioning
AI models improve the accuracy of predicting default risk and credit eligibility, enabling lenders to make informed decisions based on a borrower’s potential future behavior.
Our ‘Borrower Assessment’ AI is designed to enhance the quality of lenders’ loan portfolios by predicting the likelihood of successful loan closures.
Functions as an assistive tool for underwriting teams, augmenting their due diligence processes with data-driven insights that increase decision-making confidence.
Prioritizes fairness in lending by mitigating biases in the decision-making process, ensuring equitable treatment of all applicants.
Demo
FAQ
Increased Approval Rates
Assistive AI for Underwriting
Better Decision-Making
Default Prediction
Auto-Decisioning
Fair Lending Practices
Improves Lead Quality
Reduces manual effort
Higher Conversion Rates
Improves broker-client relationship
Lender Matching
Borrower Pre-Qualification tool for lenders and brokers streamlines the borrower-to-lender matching process by analyzing borrower profiles against lender-specific criteria in real-time.
Automatically matching borrower application with right lenders will enable an introducer/broker close the deal much faster and with relevant conversation.
By using machine learning models, the tool evaluates factors such as credit score, income, debt-to-income ratio, and other financial data to pre-qualify borrowers for loans. Allow lenders to focus on qualified leads while borrowers receive personalized loan options that fit their financial situation.
This tool enhances efficiency, reduces manual effort, and improves the overall lending experience for borrowers, lenders & brokers.
Demo
FAQ
Faster Loan Processing
Enhanced Decision-Making
Multiple File Types
Multiple Service Providers
Document Data Automation
A bank statement processing tool for lenders automating extraction & conversion of financial data from various formats (CSV, JSON, PDF, Excel, XML, HTML etc) into a standardized (canonical) format.
Automatically extracts relevant financial data from statements, such as income, expenses, and transaction history and provides structured, ready-to-use financial data.
Streamlines the loan assessment process, ensuring faster, more accurate data analysis for better decision-making.
Works with varied bank statement service providers such as Illion, DecisionLogic, Flinks, TaleFin, Yodlee, Plaid, CreditSense, Chirpp, Codat, Proviso, etc
Demo
FAQ
Bank Data Analysis
Transforms raw bank statement data into easy-to-read charts, graphs, and summaries, displaying key financial metrics such as income, expenses, and cash flow.
Categorizes and highlights recurring payments, large transactions, and spending patterns for quick assessment of borrower financial behavior.
Visual summaries enable assessors to quickly grasp the applicant’s financial health, reducing time spent on manual data analysis.
Standardized visual formats ensure consistent analysis of bank statements from various sources across all loan applications, leading to fairer assessments.
Visualization minimizes errors in interpreting complex bank statements, ensuring more accurate risk assessments.
Demo
FAQ
Faster Loan Processing
Enhanced Decision-Making
Multiple File Types
Multiple Service Providers
Sales & Marketing
Increased Conversion Rates
Lower Borrower Acquisition Costs
Product-Market Fit
User Segmentation
Data-Driven Marketing
Lead Routing
Tailored specifically for marketing teams, the AI helps create effective digital marketing strategies, ensuring that campaigns reach the most relevant potential borrowers.
Uses AI to match the right loan products from your portfolio to specific borrower needs, increasing the likelihood of successful conversions.
Leverages advanced ‘User Segmentation’ techniques to push relevant loan products to different borrower groups, enhancing the efficiency and success rates of marketing efforts.
The model incorporates “Fair Lending” principles to ensure that biases do not impact who is targeted or approved, promoting equitable access to lending opportunities.
Demo
FAQ
Funding Workflows
Inspired by successful cross-selling techniques from retail and e-commerce, we have adapted this strategy to the lending industry, helping lenders circulate more capital.
Our AI leverages borrower data to identify cross-selling opportunities, allowing lenders to offer additional loans to existing borrowers with minimal effort.
By analyzing borrower history and performance in servicing past loans, the AI makes personalized loan recommendations that are tailored to individual borrower needs and financial behaviors.
Personalized loan offerings through the ‘Loan Recommendation’ AI enhances targeted marketing campaigns' effectiveness, resulting in higher conversions and improved return on investment (ROI).
Demo
FAQ
Borrower Retention
Increased Sales
Data-Driven Personalization
Cross-Sell
Targeted Marketing Campaigns
Better Resource Utilization
Fair Lead Distribution
Faster Loan Processing
Improved Lead Matching
Increased Efficiency
Marketing Journeys
Distributes loan applications evenly among assessors to avoid workload imbalances, ensuring efficient processing across teams.
Automatically assigns loan applications based on the geographic location of the borrower, connecting them with the most relevant loan officer or branch.
Intelligent matching algorithms ensure that loan applications are assigned to the right representatives, improving the chances of successful conversions.
When necessary, loans can be automatically reassigned based on officer availability, performance, or other predefined criteria, ensuring quicker turnarounds and avoiding delays.
Lenders can tailor the assignment logic based on specific business needs, such as prioritizing certain loan types or borrower profiles.
Demo
FAQ
Increased Borrower Traction
Compliance Assurance
Optimized Loan Terms
Enhanced Borrower Experience
Cross-Sell Strategies
Utilizes AI to adjust loan terms like interest rates, repayment periods, loan amounts, and repayment flexibility based on borrower profiles and initial underwriting results.
Analyzes borrower data to provide personalized loan offerings that align with borrower needs and financial capabilities.
Ensures all loan terms adhere to lending regulations, mitigating compliance risks for lenders.
Adapts loan conditions in real-time based on borrower interaction, credit behavior, or market changes, offering a customized approach.
Seamlessly integrates with the initial underwriting process to offer flexible loans immediately after assessment, reducing the need for manual intervention.
Demo
FAQ
Payments & Collections
Improved Risk Assessment
Early Warning System
Data-Driven Decision Making
Reduced Delinquency Rates
Enhanced Underwriting Models
Collection Workflows
Utilizes AI to analyze loan performance post-servicing, focusing on identifying patterns, anomalies, and contributing factors to loan defaults.
Reviews borrower behavior, financial circumstances, and external factors that led to defaults, uncovering frequent patterns that might have been missed during initial underwriting.
Leverages historical data and AI-driven insights to detect subtle trends, such as borrower demographics, loan types, or economic conditions that correlate with higher default rates.
Identifies deviations in borrower behavior, such as irregular payments or cash flow issues, that can signal additional defaults conditions in the lending process, apart from the frequent ones.
Provides actionable insights to refine and adjust underwriting models (See:
Credit Decisioning Logik
), leading to more accurate risk assessments for future loans.
Demo
FAQ
Compliance
Regulatory Risk Mitigation
Error free Loan Validation
Audit-Ready Documentation
Reduced Legal Costs
Compliance Management
A dynamic rules engine-based compliance checker, automates the validation of loan applications against regulatory credit protection laws and regulatory standards before approval.
Applies a set of dynamic, customizable rules that can be easily updated to reflect changes in regulations or lending policies.
Provides real-time compliance validation during the loan approval process, ensuring swift and accurate decisions without manual intervention.
Automatically generates a detailed compliance documentation trail, serving as proof of adherence to credit protection laws and making audits seamless.
The engine integrates smoothly with existing loan management systems, helping lenders ensure compliance without disrupting workflows
Supports compliance across different regions or countries by allowing the rules to be adjusted for specific regulatory environments.
Demo
FAQ
Delivering
Custom Lending Solutions
With
Salesforce + AI
Lending Automation /
Digital Lending
Loan Origination & Onboarding
Loan Management
Loan Servicing
Payment & Collections
Marketing & Sales
Document Management
Compliance & Reporting
Providing
Data-Driven Lending Systems
With
Snowflake + AI
Unified Lending Data
Data Ingestion & Pipelines
Data Modeling
Data Transformation
Analytics & Insights
Portfolio Monitoring
Data Governance
Compliance & Reporting
Integrations
with
Lending Ecosystem Products
Lead Aggregator
Document Management
Bank Data
Process Mining
Credit & Identity Verification
Accounting Software
Payment Processors & Gateways
Surveys & Email Marketing
SMS communication
These & many more. Contact Us to discuss your integration needs
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FAQ
How do you customize your AI for our lending business?
The answer to this lies in data. “Fun Fact: ML (Machine Learning) models is a method or technique used to achieve AI. Each ML model needs training with data”. Each of our Machine Learning (ML) model is trained on a certain data to build AI for you. Our ML team will do initial exploratory data analysis on your data and perform the following.
Identifying features that are important for training ML models for AI. These features could include credit scores, income levels, employment history, transaction data, bank statements, etc.
Also, we ensure steps to take care of financial data privacy and security.
Please see the
FAQ-2
for more details.
We then train our ML models on your historical data and validate their performance.
We host these ML models as APIs in your public/private cloud for integration with your existing web application/CRM such as Salesforce as AI. Please see the
FAQ-3
more detail
Finally, we measure the performance metrics of our AI to ensure they perform well in different lending situations.
As a Lending Company, how do you ensure that our financial data used for training AI models is safe?
As a matter of fact, we don’t. That’s because we don’t need your sensitive financial data as the way you see it, at all. There are two well defined steps that we take to ensure that there occurs no misstep in your AI journey with us.
Anonymizing and De-identifying Data:
We only need data that cannot be identified, so the data that we need should be stripped off of or encrypted with, all Personally Identifiable Information (PII). There must be no unique identifiers present in the dataset.
Federated Learning:
Federated Learning is a machine learning technique that enables organizations to train machine learning models on decentralized data, without the need to share data. Essentially, we train our models in your environment on your data without the data ever leaving the company's premises.
This involves sending our ML models to your servers, where they will be trained on your local data that has been
‘Anonymized’
already. The trained models are then hosted for deployment in your own environment.
How can we incorporate Customizable AI into our existing systems, like Salesforce?
Incorporating AI into an existing web application requires careful planning and development, but it can provide valuable functionality and insights, to enhance your application's capabilities. The steps for incorporating AI in a web-application/CRM typically follow these steps:
Once Customizable AI models for AI are trained, and tested, we export them in a format that can be easily hosted as APIs on cloud platforms (e.g., AWS, Azure, Heroku, Google Cloud).
Your web application (like Salesforce CRM or custom web application) can integrate with these APIs, passing data to the API. This typically involves making HTTP requests to the API from your web application code.
Web applications can then present the results to users within your web application/CRM such as Salesforce.
Once I have customized/trained and deployed AI with my existing system, do I need to maintain it on an ongoing basis?
It's very important to keep your AI up-to-date as newer data becomes available and if the underlying models are upgraded from time to time.
Once you have done this implementation there are two things that need to be kept in mind from implementation and upkeep perspective:
Scenario – 1:
The underlying ML models powering the AI may change for better implementation. In this case, we will upgrade the ML models for your web application to use.
Scenario – 2:
The AI hasn’t changed, but your existing system now has generated additional financial data over a course of time. In such a case we will once again retrain AI on your new data. By doing so, the AI will predict better based on your current and relevant data.
How can we measure the effectiveness of AI based outcomes after integrations into our existing system?
Measuring the outcome on your business after integrating AI is crucial to assess the impact of the integration. AI has been evaluated on various performance metrics (like F1-Score, MAPS@K etc) to quantify the predictive performance in the overall lending lifecycle.
While we understand that this could be overwhelming as a Lending Business owner to map to actual outcomes, here are steps and key metrics to help you measure AI effectiveness:
Before deploying the AI integration, establish a baseline by measuring the KPIs without AI. This provides a point of comparison to assess the impact.
After integrating AI, continue monitoring the same KPIs to measure the impact of the integration.
Collect data on an ongoing basis to assess how the AI integration affects the selected metrics.
Track changes in conversion rates, such as click-through rates, sign-up rates, or purchase rates, as applicable. Determine if the integration increases user engagement.
On a broader timeline, track changes in revenue, such as increased sales, reduction in failures for loans serviced by borrowers, or cross-sells resulting from AI recommendations.
How do you help our lending business follow Fair & Inclusive Lending Practices in AI?
Ensuring that AI helps a lending business follow Fair and Inclusive Lending Practices and comply with relevant regulations is crucial for ethical and legal reasons. AI models are trained on data that doesn’t contain any features/variables that can potentially create bias in its predictions, thus making your business be automatically fairness-aware. Maintaining transparency in your lending considerations used while training AI, including documentation of data sources, will help your lending business with mandatory compliances.
