Paramita Bhattacharjee (Deshmukh)

Identify data quality dimensions that matter most to the business, establish specific criteria and metrics for each dimension.Ensure data quality standards. Conduct surveys to understand what customers prioritize.Address Regulatory Requirements and align your data quality standards with these regulations to ensure compliance.Implement measurement mechanisms to measure adherence Regularly assess data quality metrics against established standards and track progress over time.Continuous Improvement of data quality. Embed data quality considerations into every stage of the data lifecycle and implement quality control mechanisms at each touchpoint.Cultivate a data quality culture.

To ensure protection against cyberattacks, breaches, and fraud, selecting a RegTech partner entails stringent scrutiny of their capabilities. They should have a multi-layered defense strategy comprising encryption algorithms, robust authentication, and firewalls. Adherence to regulatory frameworks involve compliance and proactive adoption of best practices to safeguard sensitive information. Ethical data handling practices must be ingrained within their operational ethos,. A RegTech partner should offer comprehensive monitoring and reporting functionalities,continuous surveillance of regulatory requirements and performance metrics,effective alerting mechanisms,facilitating swift remediation actions and ongoing regulatory adherence.

With the evolving landscape, voice of customer is a strategy in itself. Implement agile feedback loops to continuously act upon customer feedback. Equip the team with tools acting as repositories for customer feedback and complaints.Analyze customer sentiments categorizing them as positive, negative, or neutral.Foster vibrant customer forums and communities. Implement feature voting systems and prioritize new features.Integrate customer feedback mechanisms with CRM tools, ensuring seamless communication between support teams and product developers. Conduct root cause analysis on recurring complaints, utilize techniques like fishbone diagrams and regression testing to unearth underlying issues

Just like debugging code, start by pinpointing the pain points the target customers face in their financial journeys. Utilize data analytics tools to sift through market trends and user behavior, identifying recurring issues begging for a solution.Unique Selling Points for your feature.Outline the unique functionalities and benefits your product offers, setting it apart from the sea of existing solutions. Utilize tools like the lean canvas or the value proposition canvas to sketch out your solution's core components, from customer segments to revenue streams.Conduct beta tests and feedback sessions to validate your assumptions and refine your value proposition accordingly. Iterate swiftly, treat user feedback as valuable debugging logs.

Research method is a three legged tripod. First method is user research,includes interviews to delve deep and extract their motivations, desires, frustrations, surveys gathering quantitative data, focus groups, observations of behaviors in their natural surrounding, usability test measuring meticulously their performance and feedback. Second method is hawk-eyed analytics, peering into the swirling vortex of data, deciphering patterns and trends with the clarity of a seer gazing into the future, unraveling the mysteries of user engagement and interaction.Third method is industry research, studying global and domestic market trends, competitive landscape.Choose your method wisely for they hold the keys to unraveling secrets to Fintech world

Start by clarifying what you want to learn. Are you focusing on understanding user behavior, uncovering usability issues, or testing new features? Clear goals will determine the next step of what user segment to focus on. Quoting the legend Steve Jobs "Customers don't know what they want". Customers look to have a seamless interaction with money in their daily life. They don't look at bank as a "product". Money is part of their daily transaction embedded and contextualized in different scenarios. It is crucial to understand the "thought behind the thought" of the customers when gathering insights.