“What we are able to do as a fintech company is to offer better accessibility to financial products for this group of hardworking individuals, who are currently marginalized, particularly when it comes to accessing the lending system.” But what could the fintech industry do more of to prevent this financial worry in the first place. Boden points out the importance of “simplicity, accessibility and the user experience, keeping up the ‘mission to explain’. “As long as we continue to demystify subjects which can often intimidate people such as pensions and investments, we will be fighting the good fight on financial inclusion. What the fintech industry must not lose sight of is its ability to listen to customers and adapt to meet their needs. This is an area where traditional financial services companies struggle to compete.” Sarkar also discusses how impactful financial education can be, “while highlighting the unique position employers have to support improved financial wellbeing of their workforce. For instance, our research uncovered that 77 percent of people trust their employer when it comes to information about their personal finances, and also trust their employer to keep that information confidential.
Determining the number of clusters when performing unsupervised clustering is a tricky problem. Many data sets don't exhibit well separated clusters, and two human beings asked to visually tell the number of clusters by looking at a chart, are likely to provide two different answers. Sometimes clusters overlap with each other, and large clusters contain sub-clusters, making a decision not easy. ... A number of empirical approaches have been used to determine the number of clusters in a data set. They usually fit into two categories: Model fitting techniques: an example is using a mixture model to fit with your data, and determine the optimum number of components; or use density estimation techniques, and test for the number of modes...; and Visual techniques: for instance, the silhouette or elbow rule (very popular.) In both cases, you need a criterion to determine the optimum number of clusters. In the case of the elbow rule, one typically uses the percentage of unexplained variance.
While the Securities and Exchange Commission has released some guidance on when it would consider a digital token a security, the nascent industry has complained that the SEC’s most recent comments have muddied the already murky matter. That’s why the fintech industry is lobbying hard for a bill from Ohio Republican Rep. Warren Davidson to exempt digital tokens from securities regulations, said Kristin Smith of the Blockchain Association. “That’s probably been our biggest focus,” she said. “And it will continue to be our biggest focus for the next couple of months.” Tax issues are another priority, Smith said. Because cryptocurrencies can alternately be considered currencies, securities, futures contracts or something else, their tax treatment is a complicated question that the industry hopes can be simplified soon. The IRS has issued scant guidance on how to tax digital coins, said Jerry Brito, executive director at Coin Center. Brito is hoping a pair of cryptocurrency tax bills introduced last year can advance this year.
Agile, is, after all, a relative term and fairly meaningless unless qualified. So do you know how agile your development is? One-way to embed the culture change required to answer that key question is through self-improvement (SI) processes underpinned by the right agility metrics. Agile is already closely linked to SI — let’s remember that the Agile Manifesto states: “At regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behaviour accordingly.” In other words, Agile is about continuous, team-driven SI. The fact that retrospectives is among the top five Agile techniques underscores SI’s importance (source: State of Agile report). Nevertheless, SI efforts regularly fail due to inadequate leadership and follow-through. Teams either don’t have the right tools to collect the data or that they set the wrong metrics. The latter can be especially problematic when Agile development projects are scaling.
You may have a series of sensors connected to a patient, where you’re monitoring their vital statistics which, in turn, may alert healthcare professionals or physicians as to their ongoing remote treatment and care. Indeed, smart data has many stories to tell, but we may not necessarily be privy to its journey. Moreover, in the evolution of smart objects or things, we may need the support of “smart agents” – autonomous entities that have been empowered to make decisions for us. However, in our current design doctrine human interaction is still needed. ... Of course, we’ve also empowered our smart agents to learn – a true cause and effect paradigm, in turn, slowly diminishing the need for human intervention and, again, realizing a truer definition of “machine learning.” Agents will also use blockchain technology to provide a ledger – an historical reference to what they have learned and might know for future situations – yes, predictive analytics is another reality. Our smart data is a diverse collection of values that offer many insights into the various journeys undertaken by our smart agents.
It turns out that a policyholder’s residence is a surprisingly good predictor of the likelihood that he or she will make a claim. “We found that features visible on a picture of a house can be predictive of car accident risk, independently from classically used variables such as age or zip code,” say Kidziński and Kita-Wojciechowska. When these factors are added to the insurer’s state-of-the-art risk model, they improve its predictive power by 2%. To put that in perspective, the insurer’s model is better than a null model by only 8% and is based on a much larger data set that includes variables such as age, sex, and claim history. So the Google Street View technique has the potential to significantly improve the prediction. And the current work is merely a proof of principle. The researchers say its accuracy could be improved using larger data sets and better data analysis. The researchers’ approach raises a number of important questions about how personal data should be used. Policyholders in Poland might be startled to learn that their home addresses had been fed into Google Street View to obtain and analyze an image of their residence.
There are several projects underway to cure, understand, or otherwise ameliorate the symptoms of different cancers - three of which in the DOE specifically use machine learning, as well as a broader machine learning cancer research program known as CANDLE. "In this case, the DOE and [NIH's] National Cancer Institute are looking at the behavior of Ras proteins on a lipid membrane - the Ras oncogenic gene is responsible for almost half of colorectal cancer, a third of lung cancers.” Found on your cell membranes, the Ras protein is what “begins a signalling cascade that eventually tells some cell in your body to divide,” Streitz said. “So when you're going to grow a new skin cell, or hair is going to grow, this protein takes a signal and says, ‘Okay, go ahead and grow and another cell.’” In normal life, that activity is triggered, and the signal is sent just once. But when there’s a genetic mutation, the signal gets stuck. “And now it says, grow, grow, grow, grow, again, just keep growing. And these are the very, very fast growing cancers like pancreatic cancer, for which there's currently no cure, but it's fundamentally a failure in your growth mechanism.”
It’s hardly the first security issue in security and surveillance cameras, which hold sensitive data and video footage ripe for the taking for hackers. In July, IoT camera maker Swann patched a flaw in its connected cameras that would allow a remote attacker to access their video feeds. And in September up to 800,000 IP-based closed-circuit television cameras were vulnerable to a zero-day vulnerability that could have allowed hackers to access surveillance cameras, spy on and manipulate video feeds, or plant malware. “Security cameras continue to be the oxymoron of the 21st century,” Joe Lea, vice president of product at Armis, in an email. “This is a perfect storm of a security exposure for an IoT device – no authentication, no encryption, near impossible upgrade path. We have to stop enabling connectivity over security – this is a defining moment in how we see lack of security for devices and lack of response.” In a comment to Threatpost, Marrapese said that vendors have a big part to play when it comes to doing more to secure their connected devices.
We have been discovering the value of diversity of thought through programs such as IBM’s new collar initiative and the San Diego Cyber Center of Excellence (CCOE)’s Internship and Apprenticeship Programs. IBM’s initiative and the CCOE’s program rethink recruiting to pull workers into cybersecurity from adjacent disciplines, not just adjacent fields. Toward the end of my stay at Intuit, I participated in a pilot program that brought innovation catalyst training to leaders outside of product development. Innovation catalysts teach the use of design thinking to deliver what the customer truly wants in a product. While learning the techniques I would later use to coach my teams and tease out well-designed services — services that would delight our internal customers — I was struck by an observation: People of different job disciplines didn’t just solve problems in different ways, they brought different values and valued different outcomes.
Quote for the day:
"Your first and foremost job as a leader is to take charge of your own energy and then help to orchestrate the energy of those around you." -- Peter F. Drucker