Right-Party Contact Rate for Indian NBFC Collections: AI Timing vs Manual Dialler Benchmarks

Cover image for the blog "Right-Party Contact Rate for Indian NBFC Collections: AI Timing vs Manual Dialler Benchmarks." Collections agents monitor borrower outreach performance on digital dashboards displaying AI-driven call scheduling, contact analytics, and collection metrics. The visual represents how AI optimizes call timing to improve right-party contact rates compared with traditional manual dialler workflows in Indian NBFC collections.

TL;DR Every collections operation has a single metric that determines whether agent hours convert into recovered rupees or vanish into unanswered calls and wrong-party conversations. For Indian NBFCs operating personal loan, two-wheeler, and microfinance portfolios, that metric is the right-party contact rate: the percentage of outbound attempts that reach the actual borrower. The gap between […]

AI Charged-Off Debt Recovery for US Banks: Improving ROI on Legacy Portfolios

Illustration representing AI-powered charged-off debt recovery for US banks, showing how machine learning models analyse legacy portfolios to prioritise accounts, optimise recovery strategies, improve contact effectiveness, and increase recovery ROI while reducing collection costs.

TL;DR A charged-off portfolio on a US bank’s balance sheet has a specific financial character. The accounts have been written down. The provision has been taken. The loss is already on the books. Recovery at this stage is pure upside against a cost base that has already been absorbed. The standard model for working that […]

AI Self-Learning Models for NBFC Collections: How the Technology Works

Illustration representing self-learning AI models for NBFC collections, showing continuous feedback loops where borrower interactions, repayment outcomes, and collection performance data are used to automatically refine risk predictions, treatment strategies, and recovery decisions over time.

TL;DR A propensity model deployed on a personal loan portfolio in January was trained on 18 months of historical payment data. At deployment, its scores were accurate. Recovery rates improved through the first quarter. By August, the picture has changed. Payment rates in the mid-propensity band have dropped. The model continues to route accounts to […]

MLOps for SA Credit Providers: What AI Collections Implementation Looks Like

Illustration representing MLOps for AI-driven collections in South African credit operations, showing the end-to-end lifecycle from data preparation and model development to deployment, monitoring, governance, retraining, and continuous performance improvement.

TL;DR Building an AI collections model and running one are two different disciplines. A model that performs well in development can fail in production for reasons that have nothing to do with the quality of the underlying algorithm. The training data becomes stale as the portfolio evolves. The portfolio composition shifts toward product types the […]

Credit Union Collections AI: Balancing Member Relationships and Recovery

Illustration of AI-powered collections for credit unions, showing how predictive analytics helps balance member relationships and recovery outcomes through personalized treatment strategies, repayment support, and risk-based collections prioritization.

TL;DR The member who is 45 days past due on their auto loan is also the member who has held a savings account since 1998. Their spouse has a mortgage with the credit union. Their two adult children opened their first checking accounts at the same branch three years ago. The family’s total deposit relationship […]

AI for Early Bucket Collections: How US Community Banks Reduce 30-60 DPD Loss

Illustration of AI-powered early bucket collections for US community banks, showing collections teams using predictive analytics and customer behavior data to identify repayment likelihood, prioritize outreach, and improve delinquency recovery outcomes.

TL;DR A US community bank with a $180 million consumer loan portfolio sees between 800 and 1,200 accounts enter the 30-60 DPD bucket every month. The collections team has three full-time agents. With current tools and contact infrastructure, they can realistically work about 600 accounts in a month. The remaining 200 to 600 accounts carry […]

Propensity Scoring for South African Collections: A Practical Introduction

Illustration of propensity scoring in South African collections, showing how AI models assess customer repayment likelihood, segment delinquent accounts by risk, and prioritize collection treatments to improve recovery rates and operational efficiency.

TL;DR A collections manager at a South African retailer credit provider works a 30-60 DPD portfolio of roughly 8,000 accounts every month. Her current system ranks them by outstanding balance. Her team contacts the highest balances first and works down the list. By month end they have reached about 60% of the accounts. The rest […]

Real-Time Collections Decisioning for India’s Digital Lending Market

Illustration of an AI-powered collections decision engine for India's digital lending market, showing real-time borrower segmentation, risk assessment, communication channel selection, and automated collections actions based on customer behavior and repayment likelihood.

TL;DR At 2:17pm on the 5th of the month, a borrower receives a salary credit notification. Her personal loan EMI was due on the 1st. She is now four days past due. At the moment her salary lands, her propensity to pay is at its highest point for the rest of the month. She has […]

Collections AI for South African Credit Providers: How the Technology Works

Illustration showing AI-powered debt collections technology for South African credit providers, with customer payment data, risk scoring, automated communication workflows, and collections prioritization working together to improve recovery rates and compliance.

TL;DR A collections supervisor at a South African credit provider starts most Monday mornings the same way. She opens the delinquency report. She looks at the accounts between 30 and 90 days past due. She divides them into calling batches for the week, organised by balance with the larger balances worked first. Her team moves […]

Champion-Challenger AI Testing for Indian NBFCs and Fintechs

A cover image of a blog that talks about Champion-Challenger AI Testing for Indian NBFCs and Fintechs.

TL;DR Every NBFC collections head reaches the same decision point eventually. The current AI model has been running for eight months. Recovery rates are acceptable. The data science team has built a new propensity model that, in backtesting on historical data, outperforms the current model by a meaningful margin across the 30-60 DPD bucket. The […]

Tarika Bhutani

Senior Director – Sales and Marketing Operations

Tarika is a market development leader driving global growth through strategic partnerships and go-to-market initiatives.

 

She focuses on expanding enterprise adoption of AI solutions across international markets, working closely with partners and clients to enable data-driven transformation.

 

Her work centres on scaling enterprise AI through partner-led growth and direct customer engagement, supporting organisations in implementing impactful, data-driven solutions worldwide.

Vipin Johnson

Vice President – Customer Acquisition

Description Goes Here

Rajnish Ranjan

Vice President, Head – Data Science

Rajnish brings over two decades of experience leading data-driven transformation across Fortune 500 organisations.

 

His career spans senior roles at HSBC, Zafin, Cisco, TCS, Nielsen, iQuanti, Symphony, Supervalu, and Harman, delivering measurable cost savings, operational efficiencies, and revenue growth.

 

With experience across banking, retail, telecom, pharma, CPG, and digital marketing, he leads cross-functional teams at iTuring.ai to deliver advanced analytics, machine learning, and AI solutions.

Aishwarya Hegde

VP Operations & Content Head

Aishwarya has been instrumental in building iTuring.ai from inception and continues to manage core operations across the organisation. Her responsibilities span project operations, financial planning, and evaluating future expansion opportunities.

 

Prior to iTuring.ai, she worked with Market Probe and WNS Research & Analytics, delivering high-impact decision support and actionable analytics for IBM with a record of zero errors.

 

Aishwarya holds a postgraduate degree in Data Science and Machine Learning from Manipal University.

Bryan McLachlan

Managing Director – Africa

Bryan has 30 years of experience driving innovation and growth across technology, banking, insurance, and retail.

 

Prior to iTuring.ai, he held executive leadership roles at Instant Life, AIG, Nedbank, FNB, and TransUnion. He focuses on enabling enterprises to adopt AI and machine learning within trusted, governed, and risk-managed frameworks.

 

Bryan holds a Master’s degree in Commerce from the University of Johannesburg.

Mohammed Nawas M P

Co-Founder, VP Product Development

Nawas brings 20 years of experience in designing and delivering cloud-native software and data systems. He has held senior technology roles at HCL, Radisys, Kyocera, and Mindtree, leading large development teams and complex product builds.

 

At iTuring.ai, he oversees product roadmap and customer delivery, applying cloud-first thinking, deep systems expertise, and a focus on building robust, scalable AI solutions that challenge industry norms.

 

He is a graduate of Rajiv Gandhi Institute of Technology.

Amit Kumar

Amit is a technology architect with over 18 years of experience designing data-intensive systems and enterprise analytics platforms. He has built highly scalable products across open architecture models and virtualised infrastructure, aligning deep technical detail with business requirements for AI and ML solutions.

 

Prior to iTuring.ai, he held senior technical roles at Radisys and Aricent. Amit leads platform architecture with a focus on governance, lineage, and traceability.

 

He holds a First Class with Distinction BTech in Computer Science from Cochin University.

Valsan Ponnachath

President, COO and Co-founder

Valsan brings over two decades of global leadership across sales, professional services, and product operations in technology and SaaS enterprises.

 

Prior to iTuring.ai, he held senior executive roles at Fiserv, Cisco, and Sun Microsystems, most recently serving as Senior Vice President at Fiserv overseeing global system integration and international professional services. Based in California, he leads iTuring.ai’s growth in the Americas.

 

Valsan holds an MBA from the University of Nebraska and a BE in Computer Science from Bangalore University.

Suman Singh

Founder & CEO

Before founding iTuring.ai in 2018, Suman led analytics at Zafin and Fiserv as CAO and General Manager Analytics, delivering enterprise-scale solutions still running in production.

 

His work includes fraud detection systems saving clients over $19M, patented Customer Relationship Score methodology, and price optimisation recognised by the INFORMS Edelman Award (2014). He has authored multiple research papers and pioneered the data-to-value approach.

 

Suman holds a Master’s in Statistics from CCS HAU and a Bachelor’s in Agricultural Engineering from BHU.