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Financial Services

Reduce Operational Cost, Minimize Risk & Enhance Customer Experience with a Powerful AI/Data Analytics Based Financial Solution.

Harness AI for Financial Service Operations

In the fast-paced world of today, financial organizations need to make the best choices in the shortest time. Historical data on which we’ve always depended is becoming more irrelevant, as situations change in the blink of an eye. AI& Data Analytics equips FSOs with real-time insights to make decisions on the fly, understand their customers better, and present them with tailored value propositions.

Deploying sophisticated solutions within the financial services space also requires industry-specific nuances and regulatory compliance. FSOs need to strike a balance between consumer insights and customer privacy, automating tasks without losing the human touch, and ensuring compliances are met without impacting the customer experience.

Harness AI for Financial Service Operations

Top Predictions for FSOs

According to Gartner, implementing AI/Data Analytics helps financial organizations improve transaction-processing efficiency, reduce period-end close time, and effectively predict future financial results based on trends and market data.

95%

of Fortune 500 companies will converge analytics governance into broader data and analytics governance initiatives.

20%

of time saved for collaboration and high-value analytics tasks as reliance on finance analysts for routine data management tasks reduce.

75%

of dynamic data stories that unleash insights will be generated by augmented analytics techniques.


Trigger Growth with an An End-to-End Solution

Lending

Insurance

Personal Finance

Credit Risk Scoring

By using multiple data points to analyze customer behavior, income-tax history, financial history, and other transactions, AI/Data Analytics empowers lenders to determine the risk score for each customer, and accurately predict who is most likely to repay a loan.

Customer Acquisition for Online Lenders

By analyzing the behavioural data and digital footprints on the lender's digital property, AI can predict the customer's purchase intent. Lenders can identify consumers who are most likely to buy a product, those who would need some persuasion, and those who are disinterested in the products.

Debt Collection

AI-powered tools can optimize debt collection by boosting decision-making when it comes to debt collection. It can detect patterns in historical data of solvent and insolvent borrowers, provide valuable inputs to decision-makers, and make it easier to spot at-risk accounts so debt collectors are proactive rather than reactive.

Churn Management

Sift through vast volumes of data and organize information based on who is most likely to churn. AI/Data Analytics helps you uncover specific profile attributes like age, gender, income, number of products, and the campaign from which someone came. By spotting these indicators, AI can identify customers’ intent on leaving, analyze the whys and hows, and proactively develop a strategy to combat churn.

Personalization

As the demand for "next-generation" insurance rises, AI/Data Analytics can help insurers better engage their customers. With more customers eyeing personalized coverage, insurers are coming up with a new generation of offerings to improve customer relationships and limit losses.

Customer Analytics

As more customer interactions occur via digital channels, insurers can leverage dynamic consumer information to design solutions that better match clients and get ideas to market more quickly. AI and data analytics also provide scalable personalization in distribution, underwriting, claims, and services, allowing individuals to enjoy personalized experiences

Customer Engagement

Data analytics allows businesses to provide customized solutions to clients on a large scale. Companies are harnessing data insights to provide customer value in a various ways, including data-driven portfolios, AI-based recommendation systems, and tailored offers.

Cross Selling & Recommendation

Personal financiers can detect user behaviour patterns, anticipate income and expenses, and provide customized suggestions using statistics and modeling. By analyzing buyer behaviour at the account level, banks can customize their offerings to a particular demographic and boost a campaign's ROI.

AI-Based Credit Risk Scoring model for Micro Small Consumer Loans

Client Background

Our client is a global leader in mobility solutions offering digital VAS, mobile finance, and customer management solutions to both telecom operators and micro-lending institutions. These telecom and microfinance players are currently spread out across 15 African countries.

Business Objective

Our client had a customer base of 1-10 million in each of the 15 countries where they were operating. They were capturing 2TB data every single month. Assessing credit risk and credit line assignment was a huge challenge given the diversity of the loan applicants and the volume of data. The client understood that they needed an advanced solution that could leverage all the data they were collecting and define credit limits accordingly. Our client’s current risk assessment algorithm was based on simplistic internal rules. It assigned predefined credit limits to users based on fixed segments. And there was no way of reassessing a user’s credit-worthiness based on all the data that our client was collecting across the user journey.

Outcome

15%

Improvement in loan approvals over 6 months

10%

Reduction in default rates

Predictive Churn Management for Life insurance

Client Background

The client is a pan India private life insurer following a multi-channel distribution strategy with a vision to help people plan their life better. Since its inception in 2008, it has been offering a suite of insurance products and investment plans through digital and offline channels.

Business Objective

Persistency is a key metric through which a life insurer measures the effectiveness of its retention strategy. For most practical purposes, the first year persistency is defined as a number of policies still in force after one year of acquisition. The same rule applies for the succeeding years. The client’s persistency metrics (13th, 25th, 37th ) were below the industry average. Poor persistency ratios are a cause of high concern for any life insurer, given the high cost of customer acquisition and market competition. Therefore, the client wanted to understand the reasons behind poor persistency through a quantitative approach and adopt scientific measures to improve persistency. They also wanted to devise optimal renewal strategies for agent follow-ups, email reminders, SMS alerts and telephonic follow-ups.

Outcome

15%

Increase in policy persistency in 1st year

10%

Increase in revenue

Risk Augmented Personal Loan Cross-sell

Client Background

The client is one of India’s leading financial services company focused on lending, asset management, wealth management and insurance. Through its joint ventures and subsidiaries, the company employs over 20,000 employees and has established a nationwide presence across over 1400 locations.

Business Objective

Personal loans are unsecured loans generally offered at higher interest rates; in cases higher than interest rates offered by banks for similar loans. The traditional customer segment approaching the client for a personal loan has been those who are unable to secure similar loans from a bank. This inherently increases the risk of lending to a new applicant. At the same time, high interest rates make this product attractive from an ROI standpoint. An already existing customer base with previous payment track record represents a more lucrative and less risky segment for such unsecured loans. Regular email marketing campaigns were run across the existing customer base for personal loan cross-sell. Additional outreaches were made via telephonic calls. However, since the customer base was the same, the call strategy provided limited scope and conversions too were low. The client therefore desired to expand the personal loan penetration across the existing customer base, while proactively identifying new customers who were more likely to respond to personal loan offers.

Outcome

15%

Increase in conversion rates compared to those from previous campaigns

10%

crease in higher ticket size iinthe portfolio of new cross sell campaigns

Our Blogs

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How Can NLP Be Used For Supply Planning?
Understanding the Need for Demand Planning