https://www.datacamp.com/community/podcast/scaling-ai-adoption-in-financial-services

<p>In this episode of DataFramed, we speak with Shameek Kundu, former group CDO at Standard Chartered Bank, and Chief Strategy Officer &amp; Head of Financial Services at TruEra Inc about Scaling AI Adoption throughout financial services.</p>

In this episode of DataFramed, we speak with Shameek Kundu, former group CDO at Standard Chartered Bank, and Chief Strategy Officer & Head of Financial Services at TruEra Inc about Scaling AI Adoption throughout financial services.

Scaling AI Adoption in Financial Services

https://www.datacamp.com/community/blog/scaling-ai-adoption-in-financial-services

https://www.datacamp.com/community/podcast/the-future-of-responsible-ai

<p>In this episode of DataFramed, Adel speaks with Maria Luciana Axente, Responsible AI and AI for Good Lead at PwC UK on the state and future of responsible AI.</p>

In this episode of DataFramed, Adel speaks with Maria Luciana Axente, Responsible AI and AI for Good Lead at PwC UK on the state and future of responsible AI.

The Future of Responsible AI

https://www.datacamp.com/community/blog/the-future-of-responsible-ai

https://www.datacamp.com/community/news/ai-in-healthcare-an-insiders-account-with-arnaub-chatterjee-4jljyogpays

Arnaub Chatterjee, a Senior Expert and Associate Partner in the Pharmaceutical and Medical Products group at McKinsey & Company, discusses cutting through the hype about artificial intelligence (AI) and machine learning (ML) in healthcare.

AI in Healthcare, an Insider's Account (with Arnaub Chatterjee)

https://www.datacamp.com/community/news/an-intuitive-introduction-to-support-vector-machines-using-r-part-1-wbzf8z3i4p

About a year ago, I wrote a piece on support vector machines as a part of my gentle introduction to data science R series. So it is perhaps appropriate to begin this piece with a few words about my motivations for writing yet another article on the topic. Late last year, a curriculum lead at…

An intuitive introduction to support vector machines using R – Part 1

https://www.datacamp.com/community/news/deep-learning-at-nvidia-with-michelle-gill-qo0qghjoik

The modern superpower of deep learning and where it has the largest impact, past, present and future, filtered through the lens of Michelle Gill's work at NVIDIA.

Deep Learning at NVIDIA (with Michelle Gill)

https://www.datacamp.com/community/news/tensorflow-tutorial-epj9kzri90p

In this TensorFlow beginner tutorial, you'll learn how to build a neural network step-by-step and how to train, evaluate and optimize it.

TensorFlow tutorial

https://www.datacamp.com/community/news/the-10-statistical-techniques-data-scientists-need-to-master-ed6o27bk1b9

In this article, I present 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.

The 10 Statistical Techniques Data Scientists Need to Master

<center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center><br>
<p>The financial services sector sits on a treasure trove of data. It’s no wonder that the industry is rife with promising artificial intelligence and machine learning use cases. If rolled out successfully, AI can generate a value of up to <a target="_blank" href="https://www.mckinsey.com/industries/financial-services/our-insights/ai-bank-of-the-future-can-banks-meet-the-ai-challenge">$2 trillion in the banking industry</a> according to a McKinsey estimate.  In a <a target="_blank" href="https://www.datacamp.com/resources/webinars/scaling-ai-adoption-in-financial-services-1">recent webinar</a>, Shameek Kundu, former Group CDO of Standard Chartered and current Chief Strategy Officer at TruEra, outlined how to accelerate the value of AI in Financial Services.</p>
<p>Shameek explained that the value of data science and AI in financial services can be grouped into three major buckets:</p>
<ul>
<li><strong>Higher revenues</strong> through better customer experience and better decision making</li>
<li><strong>Lowering cost</strong> through more effective risk management and improved operational efficiency</li>
<li><strong>Uncovering unrealized potentials</strong> and business models.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1639438596/image1_a4pyy3.png" /></center>

<h1 id="ai-application-in-financial-services-is-broad-but-shallow">AI Application in financial services is broad but shallow</h1>
<p>Today, many AI projects in financial services remain in their infancy, unable to reach the end goal of full deployment due to a <a target="_blank" href="https://www.datacamp.com/community/blog/5-obstacles-standing-in-the-way-of-ai-adoption-in-financial-services">variety of reasons</a>. These can range from technical issues like limited data availability and quality, technological bottlenecks in deploying AI systems—to non-technical obstacles like the lack of data talent and limited trust in AI systems.</p>
<h1 id="addressing-the-lack-of-trust-in-ai-systems">Addressing the lack of trust in AI systems</h1>
<p>While complex machine learning models like <a target="_blank" href="https://www.datacamp.com/tracks/deep-learning-in-python">deep learning models</a> can provide profound solutions to previously intractable problems like <a target="_blank" href="https://www.nature.com/articles/d41586-020-03348-4">protein folding</a>, they are not great at explaining their predictions.</p>
<p>These black boxes pose societal risks if their results are implemented blindly in high-stake decisions like <a target="_blank" href="https://www.fatf-gafi.org/media/fatf/documents/reports/Opportunities-Challenges-of-New-Technologies-for-AML-CFT.pdf">anti-money laundering</a> and credit scoring. Not knowing how black-box models arrive at their predictions, end-users find it hard to trust their outputs, especially given high-profile incidents of <a target="_blank" href="https://www.datacamp.com/resources/whitepapers/data-literacy-for-responsible-ai">bias AI systems</a>.  </p>
<p>According to Shameek, three key actions can foster trust in AI.</p>
<h3 id="demystify-machine-learning-through-education">Demystify Machine Learning through Education</h3>
<p><i>General education for all</i>
Education is the long-term strategy to address the fear towards AI. As internal stakeholders, regulators, and customers gain a general understanding of how AI impacts them, they become more likely to accept AI.</p>
<p><i>Specialized education to address the skills gap</i>
The lack of technical talent prevents companies from implementing AI systems at scale. As such, companies aiming to extract value from their AI systems must be willing to hire the data talents or provide specialized training to upskill existing employees.</p>
<h3 id="set-up-internal-guardrails-for-ai-systems">Set Up Internal Guardrails for AI Systems</h3>
<p>Internal guardrails serve as the guidelines for implementing, evaluating, and monitoring AI systems.</p>
<p>These guardrails have three major components.</p>
<ul>
<li><strong>A set of guiding principles</strong> defines the boundaries of what AI can do within the company.</li>
<li><strong>Enablers for internal stakeholders</strong> facilitates the safe implementation of AI</li>
<li><strong>Standard tools and technologies</strong> evaluate the AI systems for fairness</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1639438596/image3_iq6jur.png" /></center>

<h3 id="use-tools-and-technologies-to-improve-ai-quality">Use tools and technologies to improve AI quality</h3>
<p>Gaps in the AI lifecycle cause quality issues to creep into AI systems. Fortunately, there are tools to mitigate them.</p>
<p><i>Explaining black-box models</i>
The field of <a target="_blank" href="https://www.datacamp.com/community/news/explainable-ai-xai-d8tueh7c60j">explainable AI</a> remains an active field of research. It holds the promise to open up the black boxes, demystifying and building trust in the prediction-generation process.</p>
<p><i>Addressing the low data and labels quality</i>
A student might fail a test on an unfamiliar topic; a machine learning model can underperform if trained on the wrong data. AI systems can fail if the data used to train an AI system differs drastically from the data it sees in deployment. Microsoft’s python <a target="_blank" href="https://techcommunity.microsoft.com/t5/azure-ai/responsible-machine-learning-with-error-analysis/ba-p/2141774">Responsible AI Widget</a> can help identify and solve such problems.</p>
<p><i>Testing model transparency and fairness</i>
Models can be unfairly biased against a population, like a minority race or a particular gender, due to the hidden biases in the data used to train the model. Open-sourced solutions like <a target="_blank" href="https://github.com/fairlearn/fairlearn">fairlearn</a> and IBM’s <a target="_blank" href="https://github.com/Trusted-AI/AIF360">AI Fairness 360</a> can help to address these gaps.</p>
<h1 id="conclusion">Conclusion</h1>
<p>Building trust in AI within a company takes collaboration and time. Organizations need to upskill AI talent and set up safeguards for AI safety, while AI practitioners need to fully utilize tools available to them to make AI fair for all. Only when AI successfully wins the trust of its users can AI systems see widespread adoption.</p>
<p>If you are interested in the application of AI in financial services, make sure to check out Shameek’s on-demand webinar on “<a target="_blank" href="https://www.datacamp.com/resources/webinars/scaling-ai-adoption-in-financial-services">Scaling AI Adoption in Financial Services</a>”. It offers insights into the applications of AI and practical advice for accelerating its adoption.</p>
<br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center>

https://www.datacamp.com/community/blog/building-trust-in-ai-to-accelerate-its-adoption

Building trust in AI is key towards accelerating the adoption of data science and machine learning in financial services. Learn how to accelerate the development of trusted AI within the industry and beyond.

Building trust in AI to accelerate its adoption

<p>Today, high-performing insurance organizations are leveraging data science to advance their businesses in a competitive environment. In a <a target="_blank" href="https://www.datacamp.com/resources/webinars/future-of-data-science-in-insurance-1">recent webinar</a>, Sudaman Thoppan Mohanchandralal, Regional Chief Data and Analytics Officer at Allianz Benelux discussed the current state of data science in insurance and exciting use-cases of machine learning in the industry.     </p>
<h1 id="why-data-science-is-valuable-in-the-insurance-industry-today">Why Data Science is Valuable in the Insurance Industry Today</h1>
<p>Recently, the industry’s demand has shifted to more personalized services rather than those that appeal to mass markets. This <a target="_blank" href="https://www.mckinsey.com/industries/financial-services/our-insights/how-traditional-insurance-carriers-can-disrupt-through-personalized-marketing">McKinsey report</a> describes this shift Sudaman refers to. It explains how deep one-to-one relationships can be built with consumers at scale through strategic personalized marketing and service offerings.</p>
<p>These changes in needs and relationships are made possible by the massive growth of available data for individual customers as well as advances in machine learning and resources. In <a target="_blank" href="https://www.ibm.com/thought-leadership/institute-business-value/report/insurance-data-personalization">this article</a>, IBM discusses how insurers observe a 22-25% improvement in time to profitability when creating personalized services relative to mass-appealing products. These products can only be developed at scale using machine learning.  </p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1638880876/image3_lya3kn.png" /></center><br>
<br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center>
<h1 id="the-state-of-data-science-in-insurance">The state of data science in insurance</h1>
<p>Sudaman explains in the webinar that machine learning provides value when it is leveraged for predictions rather than causal inference and when the problem is sufficiently self-contained. As of today, machine learning fails when organizations attempt to use these algorithms as a crystal ball or black box rather than a tool to inform humans on directions to explore. Here’s a glimpse of some of the use-cases covered in the webinar:</p>
<h3 id="1-fraud-detection-in-insurance-claims">1. Fraud Detection in insurance Claims</h3>
<p>Machine learning performs very well on prediction tasks with a lot of data. Therefore, <a target="_blank" href="https://www.datacamp.com/courses/fraud-detection-in-python">fraud detection</a> is a great use case to train and deploy a classification algorithm such as logistic regression or a decision tree. This system can be used to flag claims that look suspicious. This makes fraud prevention significantly more manageable for an analyst as it will augment their workflow by surfacing the claims most likely to be fraudulent.</p>
<h3 id="2-identifying-customers-for-retention-activities">2. Identifying Customers for Retention Activities</h3>
<p>Another important use case for improving the scale of any organization is improving <a target="_blank" href="https://www.datacamp.com/courses/machine-learning-for-marketing-in-python">customer retention</a>. Machine learning can help to identify which customers are at risk of leaving your organization. Identifying these customers at scale and providing incentives for them to stay will lead to better customer lifetime value in the long run—enabling deeper personalization.</p>
<h3 id="3-optimizing-pricing-with-time-series-data-risk-premium-modeling-">3. Optimizing Pricing with Time Series Data (Risk Premium Modeling)</h3>
<p>Understanding a customer’s risk to file a claim is critical in determining the correct price and structure of a plan to provide them. All insurers are doing this at some level. With the expansion of available data, model accuracy to determine a customer’s risk profile can be improved to offer more <a target="_blank" href="https://www.datacamp.com/courses/life-insurance-products-valuation-in-r">competitive pricing</a> to individuals.</p>
<h3 id="4-future-disease-prediction">4. Future Disease Prediction</h3>
<p>Similar to the previous use case, understanding future disease risks is critical in portfolio optimization and pricing. <a target="_blank" href="https://www.lloyds.com/~/media/files/news-and-insight/coronavirus-hub/product-simplification-report/lloyds_product_simplification_report_final.pdf?la=en">This Lloyd’s of London report</a> discusses how the insurance industry will have to pay out upwards of $100 billion in COVID-related claims. While predicting the pandemic in advance would have been challenging, understanding and predicting health outcomes will enable insurers to prioritize and personalize their services.</p>
<h3 id="5-portfolio-optimization">5. Portfolio Optimization</h3>
<p>The final use case we will discuss is portfolio optimization. <a href="https://www.datacamp.com/courses/introduction-to-portfolio-risk-management-in-python">Portfolio management</a> consists of a few steps: identifying meaningful groupings of risk to be analyzed, informing optimal decisions, and identifying opportunities. Each of these subtasks is solvable through data science optimization techniques.</p>
<p>If you want to learn more about the future of data science in insurance, make sure to tune in to the webinar <a target="_blank" href="https://www.datacamp.com/resources/webinars/future-of-data-science-in-insurance-1">here</a>.</p>
<br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center>

https://www.datacamp.com/community/blog/data-science-in-insurance-today

The insurance industry is rife with data and data science use-cases that provide value. In a recent webinar, Allianz Benelux Regional Chief Data & Analytics Officer Sudaman T M explored the state of data science in the industry today.

Data Science in Insurance Today

https://www.datacamp.com/community/tutorials/tutorial-gradient-descent

Learn how gradient descent works and how to implement it.  

Gradient Descent Tutorial

https://www.datacamp.com/community/tutorials/tutorial-for-recurrent-neural-network

Learn about the most popular deep learning model RNN and get hands-on experience by building a MasterCard stock price predictor. 


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