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Advocating for fairness and transparency in financial services AI 

Financial services institutions are increasingly using artificial intelligence (AI) to automate and augment their decisions. But when it comes to which AI algorithms to use, fairness and transparency must factor into the equation. Black box AI: a cautionary tale Some of the algorithms under heavy consideration today are called “black box” algorithms. While black box...Read more

Measuring your way to failure: Thinking beyond your model metrics before deployment 

Companies across the Financial Sector are using AI and Machine Learning to model customer behaviors, avoid risk, and streamline critical business processes. Consequently, building and testing Machine Learning models has become an important discipline for many financial institutions. Traditionally, the success of Machine Learning models, and often AI algorithms, is measured by key metrics that...Read more

Securing AI and ML projects: Data and cyber risk management 

As Artificial Intelligence and Machine Learning continue to cement themselves as foundational resources for growth and transformation across the financial services industry, organizations must account for the added influx of data flooding into their enterprises. Every data science achievement must also account for how we secure and protect that data, making embedded data security risk management a far more pronounced need...Read more

From Idea to Value: A process for managing the data science lifecycle in the enterprise   

As we enter the new decade, one thing is clear: the explosive growth of data science and AI has made the effective application of them a critical differentiator for any enterprise. Despite the near-universal acknowledgement of this phenomenon and the major investments being made, many enterprises struggle to deliver sustained value on their data science...Read more

The good and the bad of off-the-shelf AI 

AI solutions aren’t all that different from investments—there are plenty of options, discernable levels of risk, and ample room for growth in AI adoption, but every organization has a custom portfolio built for its specific needs. Building your own AI models isn’t for everyone. Every financial service copy has its own expertise, capabilities, and resources...Read more

Build or Buy, the Value of Critical AI Partnerships 

If you’ve been keeping up with our AI Build or Buy series, you’ll notice that finding the right partnerships is at the core of each decision. The right AI partner serves an essential role of any finance technology strategy. They bring a level of expertise and dedication to the company’s data science needs that is...Read more

The most critical decision in building out enterprise AI: Build in-house or bring in a vendor 

From smart homes to robust intelligent edge ecosystems, AI is one of the hottest topics in tech and society as we embrace the new decade. While consumers are debating the benefits of one smart light bulb versus another, financial service companies are planning, adapting, and debating just how they can use artificial intelligence to empower...Read more

What’s Next for AI in Financial Services in 2020? The 5 Top Trends 

Follow Pascal Belaud on LinkedIn Right now, banking, capital markets, and insurance businesses are finding new uses for artificial intelligence. AI is helping to transform and keep ahead of a number of critical vectors, including customer experience, regulatory compliance, new markets and business models, and security. It’s no longer a “nice to have” innovation technology, it’s becoming a CX-level imperative. In...Read more