It is a welcome sign for the lending industry that transformative digital technology has become almost universal. Customers are increasingly becoming less reliant on ‘karma’ for financial assistance and can quickly arrange credit with a tap on their phone.
The future of finance depends on sustaining technological leaps given by digital. But, this is possible only by leveraging big data science to deliver a lending business model that create, deliver and capture value.
1. Building accurate risk rating models
Financial Institutions profit on taking the right risks. But finding one today involves a complicated journey of creating complex predictor and indicator models through multiple archaic and fragmented platforms. Inaccessible and siloed data across spreadsheets delays risk reporting, updates to existing risk models, increases costs and decreases operational efficiency. This makes it difficult to keep pace with dynamic business or customer needs, regulatory changes and emerging best practices.
A good loan origination system will give you a generation leap in risk assessment by creating tighter integration between model developers, model risk management and business teams. It can fetch high quality rating data on a real time basis through seamless integrations with credit rating agencies. With the help of AI driven data analytics, it can conduct intelligent scenarios/sensitivity analysis for stress testing. Lender can conduct a comprehensive impact analysis to determine potential changes to regulatory, economic capital, provisions etc and enhance ongoing monitoring through smart analytics, data visualization and real time alerts.
A microfinance lender in Asia was able to build a comprehensive credit profile in less than 60 minutes. They segmented customers in granular bucket and tested 15 models per hour (against one every two weeks using traditional techniques). Its business teams were able to codelessly create two models that were trained to identify opportunities using machine learning (ML). This minimized risks, created new credit products with affordable pricing and rates and reduced defaults by 10%.
2. Deploying intelligent chatbots as smart lender
Conversational lending through smart chatbots can drastically reduce lending costs and bring customers closer to instant fulfillment. Contextual chatbots can eliminate the need for human support from the onset. With the help of big data, lenders can train bots with NLP (natural language processing) and deep ML (machine learning) to identify customer needs and offer the best rates, service etc.
For instance, a leading financial institution in North America is using chatbots with predictive analytics and cognitive messaging to interact with and engage the company’s 45 million customers. It has boosted its conversion rates by 65% and reduced service time by 45%.
3. Robotic underwriting with automated approvals
Lenders can automate underwriting applications based on predefined parameters and integrations with multiple rating agencies. Users can manage exceptions based on rules through relevant workflow routing and manually intervene in case of deviations.
A loan origination system can monitor system deviations in real time and minimize cause for manual interventions. Users can set business rules to automatically approve conforming applications. They can also route applications automatically to relevant authority on predefined parameters like deviations, loan limits etc.
Algorithmic analytics and activity tracking aid real time decision making and helps lenders be regulatory compliant for audits with improved process transparency and approval matrix.
4. Enabling mass customization
Mass customization may sound cliche, but massive repositories of customer data are major assets for lenders. They collect vast amounts of customer data such as customer transactions, account balances, and personal preferences and spending habits. With the right analytical models, they can extract actionable insights, empowering them to deliver personalization on a mass scale.
A leading financial institution in Asia tied up with Amazon to track customer’s search, buying, viewing patters and preferences to customize contextual offers. They used powerful cross selling modelers that used frequency scoring, whitespace analysis etc. The algorithmic programs quickly screen vast amounts of data, extract valuable insights through large datasets and can be created once deployed anywhere.
5. Anti fraud checks
Analytics can help lenders identify fraudulent cases through automated processes. Routine monitoring of credit bureaus and transactions can be automated and a detailed activity trail can be generated for regulatory and audit compliance.
Leveraging data science can help lenders to identify new customer and market segments for existing offerings, identify new or unmet customer needs from existing customer and take a generational leap over the competition.