Data and advanced analytics lie at the core of every financial institution wanting to build stronger engagement capabilities. Unfortunately, many organizations continue to struggle to apply data that will improve the customer journey, or to move from reactive to proactive communication. To build a successful consumer engagement strategy, banks and credit unions have to better understand -- and in real time -- the consumer opportunities and threats that data reveals. The challenge: Most organizations have cumbersome data and analytic back offices and outdated data policies. And they lack sufficient talent to make the application of insights timely and reliable.
Deloitte found the financial services firms participating in their study's most common use cases for machine learning include the following. Predicting cash-flow events and proactively advising customers on spending and saving habits; expanding the data set for developing credit scores and applying machine learning to build advanced credit models for expanding reach and reducing defaults; providing machine-learning-based merchant analytics "as a service"; and detecting patterns in transactions and identifying fraudulent transactions as early as possible. Common NLP use cases include the following: reading documents and identifying errors for support activities such as information verification; user identification, and approvals; improving the underwriting process and capital efficiency; understanding customer queries via voice search on digital voice assistants or smartphones. Deloitte found the financial services firms participating in their study's most common use cases for machine learning include the following. Predicting cash-flow events and proactively advising customers on spending and saving habits; expanding the data set for developing credit scores and applying machine learning to build advanced credit models for expanding reach and reducing defaults; providing machine-learning-based merchant analytics "as a service"; and detecting patterns in transactions and identifying fraudulent transactions as early as possible.
The rapid rise of artificial intelligence adoption is disrupting many industries, most notably the banking and financial services sector. The many individual technologies that are included under the umbrella title of artificial intelligence are promising great opportunities for business efficiencies, but at the same time, creating new challenges. One of these new challenges is determining the best location to relocate or expand offices and service centers of banks, insurance companies, investment houses and other financial services companies that collectively are leading the national charge of incorporating AI technologies into their day-to-day operations. Not surprisingly, with the growing need for the latest in AI skillsets and the academic resources for recruiting and retraining of displaced workers, relocation firms such as Princeton, NJ-based The Boyd Co. are targeting those North American cities that offer superior academic programs in artificial intelligence. Indeed, the ability to provide an artificial intelligence workforce is emerging as a new site selection driver – and one that Boyd expects to soon extend well beyond the financial services sector.
One of the two task forces announced to be formed by the US House Committee on financial services will be investigating the use of artificial intelligence technologies (AI) for FinTech. The focus of the task force will be to examine digital identification technologies using AI to reduce fraud. It will also look into issues such as regulating ML in the financial services industry, risks associated with algorithms & big data, and the impact of automation on jobs and the economy in the US. AI has been one of the hottest technologies used by emerging FinTech players. It is used in automation, social media analytics & intelligence tools, cybersecurity, fraud prevention, and other areas.
The platform, built in house and slated to be launched later this year, is an example of the broader financial-services industry trend of using AI to detect patterns in transactions that could signal criminal behavior. The platform is cloud-based, meaning that Visa's researchers and engineers can access it online from anywhere. "One of the transformative technologies of this era is going to be AI," said Rajat Taneja, executive vice president of technology and operations for Visa, the largest U.S. card network by cards in circulation and transactions. "There is a perfect combination right now of computing resources, algorithms, data and people that's allowing this incredible innovation," he added. The banking industry is expected to be the second biggest spender on AI systems this year, behind retail, according to market-research firm International Data Corp.
"It has nothing to do with how smart you are, what school you went to, or how many PhDs you have in your innovation lab. If your team can't emotionally deal with the prospect of failing and having to wear egg on your face for five minutes, it's going to be difficult." Davyde Wachell is the CEO of Responsive, a hybrid wealth-focused startup. Backed by plug and play ventures, Responsive helps wealth managers in upgrading advisor productivity and decisions with next best actions driven by predictive analytics. Davyde studied AI in the Symbolic Systems program at Stanford and has worked in wealthtech for over 15 years, having built everything from quant research platforms to compliance automation tools. His film and opera work have been seen at Tribeca, Sundance and the Hammer Museum in Los Angeles. He lives in Vancouver his partner Holly, who works for Sanctuary AI, a humanoid robotics company. Now hit the Play button! This episode of Wealth Management Today is brought to you by Ezra Group Consulting. If your firm is evaluating new technology or looking to improve your current wealth platform, you need to contact Ezra Group. Don't spend another day using technology that doesn't offer an elegant user experience. Your advisors and clients deserve better and you can deliver it to them with the help of Ezra Group. Craig: Today on the Wealth Management Today podcast I am very happy to have Davyde Wachell, the Founder and CEO of Responsive. He's talking to us today live from the Tuscany region of Italy. Craig: Thanks for taking the time on your vacation overseas to talk on my podcast.
Personal relationships have always been the lifeblood of wealth management, but the pressure to intensify personalization has increased dramatically, according to Capgemini's most recent World Wealth Report. In fact, there is a "measurable correlation linking high-net-worth clients' personal connection to their firm and advisor and the financial performance of firms," according to the report. Despite a dip in investment performance last year, 88% of wealthy clients in the U.S. and Canada with investable assets of $1 million or more said they still had faith in their advisors, Capgemini found. "Personal connections are still the differentiating factor," says Chirag Thakral, an analyst with Capgemini. What do wealth managers need to do to strengthen their ties to clients in the digital age?
Machine learning, machine intelligence, thinking machine, electronic brain – whatever you want to call it, artificial intelligence is here to stay. Although, machines haven't completely taken over, they have slowly but surely crept into our lives affecting the way we live, communicate and ultimately work. From voice-driven assistance on a mobile phone, suggestive searches to autonomous driverless cars, we will continue to see fast-evolving technologies in the coming years. At ACCA we have a deep interest in how technology impacts the accountancy profession and how it will continue to do so in the future. This year will see the 30th anniversary of the worldwide web – meaning we are firmly part of the digital revolution; technology is something accountants cannot shy away from or avoid.
AI is making its way into many areas of the payments and financial services industries from helping banking and credit card systems detect and spot fraudulent activity, to enhancing customer service, providing hyperpersonalized credit scores and offers, and driving new forms of transactions like stores with no cashiers. In this infographic from Cognilytica we explore 6 ways AI is enhancing payments.
The financial services industry has seen an explosive growth in Artificial Intelligence (AI) to supplement, and often supplant, existing processes both customer-facing and internal. Given the potential created by rapid advancements in AI sophistication and functionality, more and more financial services firms are leveraging the technology to deploy new use cases for improved decision-making processes – particularly in the areas of anti-money laundering, fraud prevention, risk management, and lending. While the first wave of AI was generally focused on automating manually-intensive and repetitive tasks, banks are now turning to machine learning systems (ML) to uncover more dynamic ways of interpreting their vast swaths of customer data. Whereas AI, at a fundamental level, permits a machine to imitate intelligent human behavior, ML is a specific application (or subset) of AI that enables systems automatically to learn and improve – e.g., reduce errors or maximize the likelihood that their predictions will be true – without being explicitly programmed to make such adjustments. This development has an exciting potential to expand the products available to underbanked communities and improve services and customer experience as a whole.