Technology and artificial intelligence are transforming our lives at an unmatched rate. We can now automate everyday tasks, such as scheduling appointments, ordering groceries, or paying bills, with the help of AI. Furthermore, we are also seeing AI being used in areas like healthcare and transportation to provide better services and greater efficiency. The potential for AI technology is immense and it is only going to continue to grow in sophistication over time. Ritesh Mohan Srivastava, Chief Data Scientist, BharatPe, talks with Marquis Fernandes of Quantic Connect about his thoughts on the subject.
- How are companies coping with the substantially growing demand for AI and Data Science Professionals?
As the importance of data becomes better understood in the corporate world, data scientists are becoming more valuable as supply chains become increasingly data-centric. In order to aid with this, enterprises are taking one of the following approaches:
- Automation: Companies are adopting autonomous-data engineering software platforms to automate data management and related tasks.
- Emphasizing self-service analytics: Self-service analytics is a very popular method for scaling out data exploration, ensuring that data analytics are leveraged effectively across organizations. Autonomous data-related advances, alongside the work of the growing number of citizen analysts, are freeing computational data science and data engineering resources to focus on the higher-value activity of developing the next ML or AI model.
- Defining data science roles better: A major obstacle faced in employing data scientists is the fact that companies often struggle to visualize the precise job of the data science employee. This has to be improved so companies might use data scientists to their greatest potential.
- How is data today being managed when it comes to being transparent with customers?
- Companies typically use a mix of procedures to collect, access, store, and protect business-associated data. This, in turn, assists companies in producing actionable insights that can hone development.
- Data transparency is the manner in which data is being utilized in a truthful, honest, lawful, and demonstrably just manner for a valid objective. People and businesses need to recognize what information is being collected, who can access it, how it is being used, and how they can manipulate it.
- It’s not the case that customers are unwilling to share data. But they want to know why it’s being collected, and how it will be used. Trust is vital for customers. The companies that are not able to cultivate such faith in how they use personal data and fail to provide value lose out on customers.
- It’s essential that companies actively engage their clients about records being viewed by individuals and the way that they’re handled. They need to be certain that they’re transparent about the security measures in place and repeat to their customers that their records are secure with the firm.
- Given the growing use of Data Science, AI & ML, how are fintech leaders strategizing their goals for business growth?
- Artificial intelligence, machine learning, and data science create convenient access, automation, and effectiveness in boosting user productivity, all related to improving user experience. AI increases the ability of businesses to reach a larger audience and to develop repeat customers. Fintech is making use of AI and machine learning to deliver more personalized products tailored to their audience. AI and ML are also helping fintech gather more information about customer behaviour to identify any threats they might have.
- AI is assisting Fintech entities to better understand their goals through Advanced Decision Making. AI and Machine Learning technologies are facilitating data visualization and reporting, thus serving as a tool for better decision-making. It’s helping Fintech leaders accurately view an asset’s expected future performance.
- For fintech companies, planning to expand their businesses, machine learning will play a substantial role.
- Pros & Cons of Cloud adoption results in Data Management of fintech companies?
The advantages which FinTechs can derive from cloud computing are as follows:
- Geo-Redundant Cloud Data Centers: Storms, power outages, or equipment failure may deter regular business operations, resulting in lost time and productivity. Geographic redundancy will help any company continue uninterruptedly in the event of an emergency by allowing them to immediately redirect all their important applications and data to a secondary stored at another geography.
- Regulatory Compliance: If businesses adopt the modern cloud computing environment, the risk of loss from fraud and cyber-attacks can be kept at a level that is acceptable to the regulatory authorities.
- Ease of Deploying Managed Machine Learning: It is easier to arrange ML algorithms for managed machine learning on a cloud-based platform. Fintech companies that conduct ML work with ML algorithms to predict market volatility, identify promising opportunities, reduce fraud, and expand their business.
- Cloud Native: Rather than designing a brand new platform for each application, business needs can utilize microservices, which diminishes supplier lock-in and boosts mobility.
There are risks connected with cloud computing to fintech. The most substantial drawback relates to security. Identity fraud and data leaks can significantly affect the confidence of fintech services. Financial institutions are in data security because they know the risks of cyber-attacks are continuously growing and so they must take precautions. Other worries include Compliance. Compliance is a grave concern for fintech businesses because of its significance to the establishment of rigorous security measures to make sure there are no cracks for which hackers may exploit. Additionally, Compliance must not make it difficult for systems and software to function.
5. How are fintech leaders adapting to the Data Governance and Privacy laws based on their complexities?
In recent years, India has emerged as a center of innovation in FinTech. However, this has come with a number of regulations being rolled out, in order to minimize the risks. A data governance (DG) framework is a way of overseeing all areas of an organization’s data management process, data architecture, and data models, all the way down to individual tech, databases, and data repositories. Management of sensitive data and compliances is becoming increasingly important as cloud-based FinTech organisations continue to leverage ecosystem digitalization strategies, innovative data analytics techniques, and multi-organization alliances to provide concentrated financial institution services. Because FinTech companies require a robust DAG framework, they must have the means to stay updated on the ever-changing data requirements from an operational and regulatory viewpoint. A trusted FinTech data ecosystem is therefore vital, and the timeframe in which the need is massive.
