Using Predict360, banks and financial services organizations can now access the over 800,000 economic data metrics available in FRED and link those metrics to their specific risk appetite levels along with their internal Key Risk Indicator (KRIs) and operational metrics all in one system, enabling them to quickly identify and recognize when risks exceed the bank's risk tolerance or appetite level. Predict360 Risk Insights identifies existing risks operating outside of tolerance and predicts emerging risks using Artificial Intelligence (AI) to augment internal and external risk data. These insights help drive executive decision-making and enable financial organizations to increase profitability, accelerate innovation, and improve productivity. FRED is an online database of economic data series from various national, international, governmental, and commercial sources. By incorporating FRED metrics into Predict360 Risk Insight's KRI Engine, 360factors' customers can combine internal and external metrics to identify emerging risks and growth opportunities.

Banks and financial services institutions face increased competition not only from peer organizations within the industry, but also now from FinTech startups, Neobanks, and others. The way to compete is to deliver highly personalized services and innovative offerings. And increasingly, the way to do that is using AI/ML to derive data-driven insights upon which those services and offerings can be based. Financial services institutions increasingly have sought to use new data sources to expand their traditional risk analysis and make more personalized offerings to a larger customer base. Many have gone beyond traditional methods (e.g., using FICO scores, credit history, salary, and more) and developed new risk models and highly individualized creditworthiness ratings based on the analysis of additional data sources.

Parijat Banerjee is Global Business Head for the Banking, Financial Services, and Insurance (BFSI) sector at LatentView Analytics. The pandemic pushed us years forward, and the notion that every modern company is a technology company has only been reinforced by rapidly transforming business practices. We are at an apex of digital transformation, and precision analytics is driving the most successful innovation initiatives across many industries. The unequivocal rise of the connected ecosystem has forced consumers to engage across digital channels, and banking and financial services evolved almost overnight. The race to digital has always seemingly been easy, but the adoption and implementation remain an uphill climb, particularly in the financial services industry, where legacy solutions and antiquated IT infrastructure have a stranglehold on business processes. Over the last 18 months, exponential improvements in digital technology have underpinned the evolution of banking for both internal processes and customer-facing experiences.

Financial institutions today face numerous challenges--everything from intense regulatory pressures to upstarts coming at them from every direction with disruptive technology solutions--that threaten even their core businesses. According to a 2015 Capgemini report, 65% of affluent individuals will leave their current wealth management firm if an integrated channel experience is not provided. Per the 2016 Edelman Trust Barometer, from 2012 to 2016, global trust in financial services has risen by 8% to 51%--the largest increase of any industry surveyed--but it remains the lowest overall. In the U.S., 23.5 million households were unbanked or underbanked in 2015 (FDIC), while a staggering 2 billion adults globally were found to be unbanked in 2014 (World Bank). AI or artificial intelligence--what we like to call augmented intelligence--promises to make it possible for financial services organizations to respond to these imperatives, and with unexpected efficiency and agility.

The financial services industry is a dynamic space that is constantly testing and pushing novel use cases of information technology. Many of its members must balance immense demands--from the pressures to unlock continuous innovation in a landscape with cloud-native entrants, to responding to unexpected surges in demand and extend services to new regions--all while managing risk and combatting financial crime. At the same time, financial regulations are also constantly evolving. In the face of the current pandemic, we have seen our customers accelerate in their adoption of new technologies, including public cloud services, to keep up with evolving regulations and industry demands. Hand in hand with growing cloud adoption, we've also seen growing regulatory concerns over concentration risk (check out our recent whitepaper on this), which have resulted in new recommendations for customers to increase their overall operational resiliency, address vendor lock-in risks and require effective exit plans.

COVID-19 has increased customer demand for contactless transactions everywhere, but especially so in the financial services industry. After all, who wants to risk their health to sign a deposit slip? There is much to be gained by financial services organizations and customers through AI-enabled interactions. In the latest report from the Capgemini Research Institute – Smart Money: How to drive AI at scale to transform the financial services customer experience – we spoke with over 5,000 customers and more than 300 industry executives from banking and insurance. We found that financial services firms are turning more and more to AI for customer interactions and they are deriving significant benefits from doing so.

As artificial intelligence is becoming core to traditional financial services organizations no surprise: banks, investment management or leading financial advisory enterprises are in the accelerated path of researching solutions, to transform their entire businesses and innovate fast enough to provide smart, user-centric digital services that allow them to retain customers, build intelligent digital engagement and provide new revenue streams. 'Artificial intelligence and the future of Financial Services' organized by The Economist and'AI and Data Science in Trading' organized by AIDST Digital Week, to interact with executives, decision makers and thought leaders from across the finance industry. Some of my random thoughts based on the events output you will find outlined below. The need for external and more timely data has never been greater in the investment workflow. Last week I had an opportunity to participate in an AI & Data Science in Trading virtual event that brought together experts in the use of AI and advanced data analytic techniques within asset management, primarily for finding alpha, managing risk and optimizing portfolios.

Are your AI Algorithms locked down and safe? In my prior Forbes blogs, the business imperative for Board Directors and CEOs to advance their governance practices to lead forward with AI was framed. This blog shares the insights from a recent interview with Cathy Cobey, the EY global trusted AI leader, where we explore: how practicing responsible AI is stacking up, the impact of data bias and key board director questions to ensure CEO's are managing the new risks that AI presents. One of the key insights Cathy shared is that from all of her global client interactions to date, she has yet to find any organization, large or small, which has a robust inventory management process to easily identify or inventorize their AI models. This also mirrors my global research that Board Directors and CEO's don't know where their AI algorithms are.

Are your AI Algorithms locked down and safe? In my prior Forbes blogs, the business imperative for Board Directors and CEOs to advance their governance practices to lead forward with AI was framed. This blog shared the insights from a recent interview with Cathy Cobey, the EY global trusted AI leader, where we explore: how practicing responsible AI is stacking up, the impact of data bias and key board director questions to ensure CEO's are managing the new risks that AI presents. One of the key insights Cathy shared is that from all of her global client interactions to date, she has yet to find any organization, large or small, which has a robust inventory management process to easily identify or source their AI models. This also mirrors my global research that Board Directors and CEO's don't know where their AI algorithms are.

The Cognitive Computing Era will change what it means to be a business as much or more than the introduction of modern Management by Taylor, Sloan and Drucker. Are you sure you want to view these Tweets? What is an #IoT Intelligent Electronic Sensor? Why investing in #AI is one of the biggest commercial opportunities for businesses. According to a recent @PwC report, the UK's GDP will be up to 10.3% higher in 2030 as a result of #ArtificialIntelligence By @technative http://hubs.ly/H0l9WpR0