Archit Jain’s Post

🤩The #fintech industry is on the rise! 📈 It's exciting to see the latest trends that are emerging in this space. 👉One of the most exciting developments has been the rise of open banking! #OpenBanking #FinancialFreedom 🤖Additionally, the use of AI and machine learning is becoming more commonplace, providing more accurate insights and allows companies to make data-driven decisions quickly. #AI #ML #DataDriven It's an exciting time for the Fintech industry and I look forward to seeing what the future holds! 🤩 📢If you are currently on the look out for a new role and have an interest in the Fintech industry, then get in touch - I have a pool of clients that I am sure would love to have a chat with you! #recruitment #hiring #opentowork #projectmanagement #businessanalysis #softwareengineering #dataengineering #ML #MachineLearning #AI #ArtificialIntelligence

Fintech is reshaping finance and opening doors to innovative roles. AI is revolutionising fraud detection, algorithmic trading, and customer service In the financial sector. Fintech companies in the UK are actively seeking AI talents to drive innovation, improve decision-making processes, and create more secure financial transactions. We'd love to hear your experiences with how AI is influencing your recruitment strategy below. #techrecruitment #recruitment #recruiter #Fintech #AI

Key Challenges and Opportunities in Product Management for Machine Learning in Fintech Machine learning (ML) is rapidly reshaping the fintech industry. Its crucial, for product managers to stay informed about the challenges and exciting opportunities that come with it. Challenges; Ensuring data quality and accessibility; Fintech companies face the task of accessing high quality data to train and implement ML models. This can be an endeavor due to the regulated nature of financial data. Making models interpretable and explainable; It's vital for fintech companies to be able to provide explanations of how their ML models function both for purposes and to instill confidence in their customers. However this can be a task as ML models often involve processes. Navigating compliance; Fintech companies must ensure that their ML models adhere to all regulations. This challenge is intensified by the evolving landscape of regulations in the finance industry. Opportunities; Detecting and preventing fraud; ML offers potential in detecting and preventing activities within fintech transactions. This presents an opportunity for fintech companies as they strive to mitigate losses caused by fraud. Assessing and managing risk; ML can help fintech companies assess and manage risk effectively providing them with insights, for making informed lending decisions while safeguarding against potential losses. ML has the potential to customize products and services according to each customers needs. This enables fintech companies to enhance customer satisfaction and loyalty. Additionally ML can automate processes, like underwriting, KYC and AML benefiting fintech companies by cutting costs and streamlining operations. #machinelearningproductmanagement, #fintech, #productmanagement, #machinelearning, #ml, #productdevelopment, #careeradvice, #recruiting

What technologies will FinTech focus on in 2024? DZone and Techtrust founder, BK Bhāte believe Cybersecurity and adopting AI to cut costs will be at the top of the list. What does this mean for the backbone of these organizations - their technical talent? On one hand, forecasts of a hiring surge are ahead, yet we’re still witnessing unprecedented times of churn: organizations tightening budgets, cutting costs, and replacing employees with AI tools at a staggering rate. No matter the case, one thing is for sure; in 2024, FinTech companies will grapple with the scarcity of AI and fundamental data engineers. For tech leaders, our advice will be to focus on developing and creating environments that nurture fundamental skills in Data Structure, Algorithms, and Data Analysis. Knowledge of building Business Intelligence tools is helpful, too. For technologists, contributing to Open Source projects by solving bugs on platforms such as GitHub is highly desired. Check out this article in DZone to see the full predictions and gain insights about how to not just navigate but lead the evolution. #fintechjobs #fintech #aiinvestments #aihiring #techjobs #techjobsearch https://lnkd.in/eJa7jb3x

Attn: Engineers, Data Scientists and Product Managers Building a Data and AI product from scratch is a thrilling yet challenging journey. I'm excited to share insights, highlighting the critical role of a robust product management foundation in Fintech. Use-Case: Credit Risk Assessment in Fintech In Fintech, assessing credit risk accurately is paramount. Here is an example of journey to create a data-driven solution aiding lending partners in informed decisions. Key Lessons: 1. User Understanding: Deep dive into lending institutions' needs through interviews, industry reports, and historical loan data analysis. 2. Clear Goals: Define a precise objective – building an AI-powered credit risk assessment tool using alternative data sources for comprehensive borrower risk profiles. 3. Agile Iteration: Embrace an agile approach, constantly refining algorithms based on feedback and performance data from partners. 4. Data Security & Compliance: Prioritize data security and compliance, collaborating closely with legal and compliance teams. 5. User-Centric Design: Craft an intuitive tool with user-friendly dashboards, simplifying risk assessment for underwriters. 6. Performance Metrics: Define KPIs like predictive accuracy, default rates, and time saved in underwriting. Regular tracking gauges product impact. Result?  By prioritizing a solid product management foundation, one can successfully launch an AI-driven credit risk assessment tool. This will help partners leverage it for informed lending decisions, reducing default rates, and streamlining processes. Key Takeaways: - User-Centric Approach: Start with deep user understanding. - Clear Objectives: Define measurable product goals. - Agile Mindset: Embrace continuous improvement. - Compliance Focus: Prioritize data security and regulatory adherence. - Data-Driven Metrics: Monitor and measure product impact. Building Data and AI products from scratch demands dedication and adaptability. It's about overcoming challenges, ensuring compliance, and delivering real value. Have you encountered similar experiences in your data product journey? Share your insights!  #Fintech #DataProduct #AIProduct #ProductManagement

wonderig if the banking talent exodus is also true for the opposite direction… “Over the past two years there has been a massive migration of AI talent from big banks to big tech companies. We analyzed the LinkedIn data of 5,000 AI executives in finance (VP level and above) who have left banking and found that the typical bank loses four AI people for every five that it hires. When AI talent leaves the biggest banks, they usually leave the industry altogether, finding new roles at tech giants like Amazon, Google, and Microsoft” https://lnkd.in/dAweyMVj

The finance industry is in flux. With fintech companies popping up at record speed, digital transformation has become a necessity rather than an option. Traditional banking and finance processes are being replaced with agile, tech-driven systems designed to meet customer needs more efficiently. This shift in dynamics is creating newer positions and roles within the financial services sector. Robotic Process Automation (RPA), Artificial Intelligence (AI), machine learning...banks and financial institutions are looking for talents with knowledge in these areas more than ever before. The question here is, are we as recruiters ready to catch up with this changing landscape?  As professionals of this industry, it is our responsibility to acknowledge this shift and work towards staying relevant. We need to understand the requirements of these new-age roles, expand our search metrics, adapt to modern recruitment tools and create strategies focused on hiring talent that can drive growth in this digitally transformed environment. Keeping up with change isn't easy but remember, those who adapt thrive. #fintech

Big Banks to Big Tech: The AI Talent Exodus Is the allure of innovation driving AI execs from finance to Silicon Valley? The latest data shows a significant exodus of AI talent from big banks to tech companies. From June 2021 to June 2023, over 800 executives made the switch, with Amazon, Google, and Microsoft leading the charge. This trend is likely driven by a number of factors, including: The growing importance of AI in tech: Tech companies are at the forefront of AI development, and they are investing heavily in attracting and retaining top talent. The slower pace of innovation in banks: Banks are often slow to adopt new technologies, and this can be frustrating for AI execs who want to make a real impact. The higher salaries and benefits offered by tech companies: Tech companies are known for paying their employees top dollar, and they also offer generous benefits packages. If you're an AI exec working in a bank, it's worth considering whether you might be happier and more fulfilled in a tech company. The data suggests that there are plenty of opportunities out there, and the rewards can be significant. #aitalent #bigtech #bigbanks #innovation #careeradvice

While banks aren't rushing to hire AI executives , they are investing in the technologies more frequently. Banks that are serious about making digital transformations when it comes to AI should consider having a centralized leader to take on the job. Here's why.

Title: Revolutionizing Finance: The Role of Artificial Intelligence In recent years, the intersection of finance and artificial intelligence (AI) has sparked a revolutionary transformation in the way financial institutions operate, manage risks, and serve their customers. From algorithmic trading to fraud detection and customer service, AI is reshaping the landscape of the financial industry in unprecedented ways. 1. Algorithmic Trading: One of the most prominent applications of AI in finance is algorithmic trading. AI-powered algorithms analyze vast amounts of financial data at lightning speed, identifying patterns and trends that human traders might overlook. These algorithms can execute trades autonomously, taking advantage of market inefficiencies and making split-second decisions to maximize returns. 2. Risk Management: AI plays a crucial role in risk management for financial institutions. By analyzing historical data and market trends, AI algorithms can assess potential risks more accurately than traditional methods. Machine learning algorithms can predict market volatility, credit risks, and even identify potential fraudulent activities, enabling financial institutions to make informed decisions and mitigate risks effectively. 3. Fraud Detection: Detecting and preventing fraud is a constant challenge for financial institutions. AI algorithms excel in detecting fraudulent activities by analyzing vast amounts of transaction data in real-time. These algorithms can identify suspicious patterns and anomalies, flagging them for further investigation. 4. Customer Service: AI-powered chatbots and virtual assistants are revolutionizing customer service in the financial industry. These intelligent systems can interact with customers in natural language, providing personalized recommendations, assisting with account inquiries, and even executing transactions. By leveraging AI-driven customer service solutions, financial institutions can enhance the customer experience, reduce response times, and streamline operations. 5. Regulatory Compliance: Complying with complex regulatory requirements is a significant challenge for financial institutions. AI-powered solutions can automate compliance processes by analyzing vast amounts of regulatory data and identifying areas of non-compliance. By automating regulatory compliance tasks, financial institutions can reduce costs, minimize risks, and ensure adherence to regulatory standards. Conclusion: The integration of artificial intelligence into the finance industry represents a paradigm shift in the way financial institutions operate and serve their customers. From algorithmic trading to risk management, fraud detection, customer service, and regulatory compliance, AI is transforming every aspect of the financial ecosystem. CA Rajat Rashmi #finance