Interviews

Data Analytics will play a critical role in optimizing the fintech sector

In conversation with Neha Shivran, Chief Data & Analytics Officer, RING on how data analytics is playing an increasingly important role in the fintech ecosystem, and it is likely to continue to reshape the industry in the coming years

 

AI & Big Data are the buzzwords in the fintech sector. Can you briefly explain what big data analytics or data science means?

Big data analytics refers to the process of examining and analyzing large and complex data sets in order to uncover hidden patterns, correlations, and insights. This process involves using various tools and techniques to clean, transform, and structure the data, as well as to apply statistical and machine learning algorithms to extract valuable information from it.

Data science, on the other hand, is a multidisciplinary field that combines elements of statistics, mathematics, computer science, and domain expertise to extract insights and knowledge from data. It involves using scientific methods, processes, algorithms, and systems to extract meaningful insights from structured and unstructured data.

Both big data analytics and data science are important tools in the fintech sector, as they can help financial institutions to better understand customer behavior, assess risk, detect fraud, and optimize business processes.

 

How is Data Analytics reshaping the Fintech ecosystem?

Data analytics is having a significant impact on the fintech ecosystem, as it allows financial institutions to make more informed decisions and provide better services to their customers. Here are a few ways in which data analytics is reshaping fintech:

Improved risk management: With the help of data analytics, financial institutions can better assess risk and make more accurate predictions about future trends. This enables them to make more informed lending decisions and manage their portfolios more effectively.

Personalization of services: By analyzing customer data, fintech companies can tailor their services to meet individual needs and preferences. For example, they can provide personalized investment recommendations, offer customized insurance policies, and provide personalized credit offers.

Fraud detection and prevention: Data analytics can help financial institutions detect and prevent fraud in real-time by analyzing patterns and anomalies in transaction data. This can help prevent losses due to fraudulent activities, which can be a significant problem in the financial industry.

Cost savings: By automating many of the data analysis tasks, fintech companies can reduce costs and improve efficiency. This can be particularly beneficial for smaller fintech startups, which may have limited resources.

Overall, data analytics is playing an increasingly important role in the fintech ecosystem, and it is likely to continue to reshape the industry in the coming years.

 

How does Data Analytics help in improving the security features or mitigating the scope of fraud?

Firstly, data analytics can help financial institutions to identify patterns and anomalies in customer behavior that may indicate fraudulent activity. By analyzing large and diverse data sets, including transaction data, customer information, and social media data, data analytics can help identify patterns and trends that are indicative of fraud.

Secondly, data analytics can help financial institutions to detect and prevent fraud in real-time. By using machine learning algorithms and other advanced analytics techniques, financial institutions can monitor transactions in real-time and identify fraudulent activity before it can cause significant damage.

Thirdly, data analytics can help financial institutions to continuously improve their fraud prevention strategies. By analyzing data on past fraud incidents, financial institutions can identify common patterns and tactics used by fraudsters, and develop new and more effective fraud prevention strategies.

Finally, data analytics can help financial institutions to improve their overall security posture. By analyzing data on cybersecurity threats and vulnerabilities, financial institutions can identify areas of weakness in their security systems and take proactive steps to address them before they are exploited by cybercriminals.

Overall, data analytics is an essential tool for financial institutions looking to improve their security features and mitigate the scope of fraud.

 

With the growing adoption of technology, what are the key trends or evolution of data analytics, one might see in future?

As the adoption of technology continues to grow, there are several key trends and evolutions that we can expect to see in the field of data analytics in the future. Here are some of the most significant:

Increased use of artificial intelligence and machine learning: As more data becomes available, there will be an increased reliance on AI and machine learning to help extract insights and knowledge from it. These technologies will become more sophisticated and capable of handling increasingly complex data sets.

Greater focus on data privacy and security: As data becomes more valuable and more frequently targeted by cybercriminals, there will be a greater emphasis on protecting it. This will involve the use of more sophisticated security measures, such as blockchain technology and advanced encryption techniques.

More personalized and real-time analytics: As the volume of data continues to grow, there will be a greater need for real-time analytics that can quickly identify and respond to emerging trends and patterns. This will enable businesses to provide more personalized services to their customers and better meet their needs.

Increased automation: As the amount of data being generated continues to increase, there will be a greater need for automation in data analytics. This will involve the use of automated tools and algorithms to streamline the data analysis process and reduce the workload on data analysts.

Integration with other technologies: As data analytics becomes more integrated into other technologies, we can expect to see the emergence of new applications and use cases. For example, data analytics could be integrated with augmented reality or virtual reality to provide more immersive and interactive experiences.

Overall, these trends and evolutions are likely to drive significant changes in the field of data analytics in the coming years, with a greater focus on real-time, personalized, and secure data analysis.

 

What are Predictive analytics and will it help the Fintech sector?

Predictive analytics is a type of data analytics that involves using statistical and machine learning algorithms to analyze historical data and make predictions about future events. It involves using a variety of data sources, including customer data, financial data, and market data, to identify patterns and trends that can be used to make predictions about future events or outcomes.

In the fintech sector, predictive analytics has a wide range of applications, including fraud detection, risk management, and customer engagement. For example, predictive analytics can be used to identify fraudulent transactions by analyzing patterns and anomalies in transaction data. It can also be used to assess credit risk by analyzing customer data and predicting the likelihood of default.

In addition, predictive analytics can help fintech companies to improve customer engagement by analyzing customer data and identifying trends and patterns that can be used to personalize marketing messages and offers. This can lead to higher customer satisfaction, increased loyalty, and ultimately, increased revenue.

Overall, predictive analytics has the potential to be a powerful tool for fintech companies, enabling them to make data-driven decisions and better understand their customers and the market. By leveraging predictive analytics, fintech companies can gain a competitive advantage and deliver better outcomes for their customers.

 

How is RING leveraging Data Analytics to enhance their offering and customer experience?

RING is a fintech company that offers a payment processing platform that enables businesses to accept digital payments from their customers. The company leverages data analytics to enhance their offering and improve the customer experience in several ways:

Fraud detection: RING uses data analytics to detect and prevent fraudulent transactions. By analyzing historical transaction data, the company can identify patterns and anomalies that may indicate fraudulent activity and take action to prevent it.

Risk management: RING uses data analytics to assess credit risk and make informed decisions about whether to approve or decline payment transactions. By analyzing customer data and payment history, the company can predict the likelihood of default and take steps to mitigate risk.

Customer engagement: RING uses data analytics to personalize the customer experience and drive engagement. By analyzing customer data and purchase history, the company can tailor marketing messages and offers to individual customers, leading to higher engagement and customer loyalty.

Performance monitoring: RING uses data analytics to monitor the performance of their platform and identify areas for improvement. By analyzing platform usage data, the company can identify bottlenecks and other performance issues and take action to address them.

Overall, by leveraging data analytics, RING is able to provide a more secure and personalized payment processing platform that delivers a better customer experience. By continuing to invest in data analytics, the company can stay ahead of the competition and continue to innovate in the fast-changing fintech industry.

 

There fewer women in the Data analytics segment? What are the key initiatives which you think will encourage more women to join the sector?

Yes, there are fewer women in the data analytics segment compared to men. According to a study by the World Economic Forum, women make up only 15% of the data professionals in the industry.

To encourage more women to join the data analytics sector, there are several key initiatives that can be taken:

Education and training programs: Providing education and training programs that specifically target women can help to increase the number of women in the field. These programs can provide women with the necessary skills and knowledge to succeed in the data analytics industry.

Mentorship programs: Mentorship programs can help to provide women with the guidance and support they need to succeed in the industry. By pairing women with experienced data professionals, mentorship programs can help women to overcome the barriers they may face and develop their careers.

Networking events and communities: Networking events and communities can help to connect women with other professionals in the industry, providing them with opportunities to learn, grow, and develop their careers. These events and communities can also help to raise the profile of women in the industry and promote greater diversity.

Flexible work arrangements: Offering flexible work arrangements, such as remote work and flexible hours, can help to attract and retain women in the industry. These arrangements can help women to balance their work and personal lives, making it easier for them to pursue careers in data analytics.

Addressing unconscious bias: Addressing unconscious bias in the industry can help to create a more welcoming and inclusive environment for women. This can involve training programs to help people recognize and overcome their biases, as well as policies and practices that promote diversity and inclusion.

Overall, these initiatives can help to encourage more women to join the data analytics sector and create a more diverse and inclusive industry. By increasing the number of women in the field, we can tap into a broader pool of talent and ideas, leading to better outcomes for everyone involved.

 

What major transformation would you like to see in Data analytics over the next 10 years? 

Over the next 10 years, I would like to see several major transformations in data analytics:

Increased focus on responsible data use: With the increasing importance of data in society, there needs to be a greater emphasis on responsible data use. This includes ensuring that data is collected, analyzed, and used in a way that respects individual privacy and human rights.

Integration of AI and machine learning: AI and machine learning have the potential to revolutionize the field of data analytics by enabling more sophisticated analysis and prediction. I would like to see greater integration of AI and machine learning in data analytics to enable faster and more accurate decision-making.

Greater collaboration and sharing of data: Data analytics is increasingly becoming a collaborative effort, with multiple stakeholders working together to achieve common goals. I would like to see greater collaboration and sharing of data between different organizations and industries to achieve better outcomes for everyone.

More focus on actionable insights: While data analytics has made great strides in recent years, there is still a need to translate insights into action. I would like to see more focus on providing actionable insights that enable decision-makers to take action and achieve meaningful results.

Improved data literacy: As data becomes increasingly important in society, there is a growing need for improved data literacy among the general population. I would like to see greater emphasis on education and training programs that teach people how to work with data, interpret it, and use it to make informed decisions.

Overall, I believe these transformations would help to create a more responsible, collaborative, and effective data analytics industry that delivers better outcomes for everyone.

 

Briefly introduce RING? What is your role in the organization?

RING is an online instant credit platform that caters to millennials, Generation Z, and those without access to traditional lines of credit. It is yet another disruptive product offering from the founders of Kissht. RING customers can avail a credit of up to INR 30,000 for eCommerce transactions, bill payments, paying merchants offline, and transfer money to friends or themselves.

I am the Chief Data and analytics officer here and my role is to facilitate data driven decision making in the organization. It ranges from providing insights for efficient business decisions to machine learning solutions for credit underwriting and fraud.

 

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