Most financial institutions know it's critical to manage the ever-increasing amounts of accessible data, but many miss the potential in using that data in innovative ways. Financial institutions have a plethora of data they can access, either through their own systems or through public sources. However, many can't -- or won't -- exploit the large volumes of data, particularly the "owned" data that an organization holds about customers. This kind of data is typically called customer relationship management data, such as the purchase history tied to app installs, email addresses and postal addresses. Though financial institutions maintain and collect massive volumes of data, many firms are restricted from fully using that data because they are required to comply with stringent regulations around what can and cannot be done with customer data.
It's time for city administrations and local employers to close AI-related skills gaps. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. While there is much discussion of how artificial intelligence will continue to transform industries and organizations, a key driver of AI's role in the global economy will be cities. How cities deal with coming changes will determine which ones will thrive in the future. Many cities have plans to become "smart cities" armed with AI-driven processes and services, like AI-based traffic control systems, to improve residents' lives.
Should Elon Musk's robot-surgeon start inserting electrodes into human brains to connect humans and computers via a high-bandwidth brain-machine? What exactly are the implications for medical insurance? Should a self-driving flying taxi crash and kill civilians? These are the questions our CEO, Lizé Lambrechts, is asking. The insurance industry is developing new ways to assess and underwrite risk as artificial intelligence (AI) and automation advance.
Specialty re/insurer Canopius has partnered with technology start-up Arturo on artificial intelligence (AI) and deep-learning property analytics. The integration of Arturo's AI-powered technology will enable Canopius to gain access to the physical property characteristic and predictive analytics using the latest satellite, aerial, and ground-level data. As a result, Canopius will be able to make more informed and differentiated pricing decisions at the point of underwriting. Canopius chief digital officer Marek Shafer said: "Arturo's, AI-powered image analytics capability is hugely impressive. Canopius is excited to be harnessing this pioneering technology, which will help to fine-tune our risk selection process and improve point-of-sale underwriting."
As technology, including robots, artificial intelligence, machine learning, and other forces change the nature of work, employees will need new skills to adapt to shifting roles. Research firm Gartner predicts that employees who regularly update their skill sets and invest in new training will be more valued than those with experience or tenure. But it's not going to be easy. The World Economic Forum's "Future of Jobs 2018" report estimates that, by 2022, more than half (54%) of employees will require significant skills updating or retraining. More than one-third (35%) will need about six months to get up to speed, while nearly one in five will require a year or more of additional training.
What I saw didn't look very much like the future -- or at least the automated one you might imagine. The offices could have been call centers or payment processing centers. One was a timeworn former apartment building in the middle of a low-income residential neighborhood in western Kolkata that teemed with pedestrians, auto rickshaws and street vendors. In facilities like the one I visited in Bhubaneswar and in other cities in India, China, Nepal, the Philippines, East Africa and the United States, tens of thousands of office workers are punching a clock while they teach the machines. Tens of thousands more workers, independent contractors usually working in their homes, also annotate data through crowdsourcing services like Amazon Mechanical Turk, which lets anyone distribute digital tasks to independent workers in the United States and other countries.
AI is the most promising technology to transform the banking space. Forty-seven percent of respondents said AI will have the biggest impact, followed by just 19% saying the same for quantum computing and 17% for distributed ledgers and blockchain. The disappointing outcome for blockchain appears to be in line with recent announcements from banks: Citi has abandoned its plans to launch a crypto and Bank of America's tech and operations chief has expressed skepticism on the benefits of blockchain. Banks' workforces appear to be at different stages in terms of tech savviness.Seventy-four percent of banking respondents either agree or strongly agree that their employees are more digitally mature than their organization, resulting in a workforce waiting for their organization to catch up. However, 17% of respondents said that over 80% of their workforce will have to move into new roles requiring substantial reskilling in the next three years, compared with only 5% saying the same for the last three years.
Elliptic's dataset comprises 200,000 bitcoin transactions, with a total value of $6 billion. It will be publicly available from today, and can now be used by open-source developers and other researchers to train machine learning algorithms to spot characteristics that are unique to illicit or legitimate transactions. By helping to keep crypto within the law, it should boost its legitimacy in the eyes of governments around the world.
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.