Top 7 Data Science Use Cases in Finance


In recent years, the ability of data science and machine learning to cope with a number of principal financial tasks has become an especially important point at issue. Companies want to know more what improvements the technologies bring and how they can reshape their business strategies.
To help you answer these questions, we have prepared a list of data science use cases that have the highest impact on the finance sector. They cover very diverse business aspects from data management to trading strategies, but the common thing for them is the huge prospects to enhance financial solutions.
Automating risk management
Risk management is an enormously important area for financial institutions, responsible for company’s security, trustworthiness, and strategic decisions. The approaches to handling risk management have changed significantly over the past years, transforming the nature of finance sector. As never before, machine learning models today define the vectors of business development.
There are many origins from which risks can come, such as competitors, investors, regulators, or company’s customers. Also, risks can differ in importance and potential losses. Therefore, the main steps are identifying, prioritizing, and monitoring risks, which are the perfect tasks for machine learning. With training on the huge amount of customer data, financial lending, and insurance results, algorithms can not only increase the risk scoring models but also enhance cost efficiency and sustainability.


In recent years, the ability of data science and machine learning to cope with a number of principal financial tasks has become an especially important point at issue. Companies want to know more what improvements the technologies bring and how they can reshape their business strategies.
To help you answer these questions, we have prepared a list of data science use cases that have the highest impact on the finance sector. They cover very diverse business aspects from data management to trading strategies, but the common thing for them is the huge prospects to enhance financial solutions.
Automating risk management
Risk management is an enormously important area for financial institutions, responsible for company’s security, trustworthiness, and strategic decisions. The approaches to handling risk management have changed significantly over the past years, transforming the nature of finance sector. As never before, machine learning models today define the vectors of business development.
There are many origins from which risks can come, such as competitors, investors, regulators, or company’s customers. Also, risks can differ in importance and potential losses. Therefore, the main steps are identifying, prioritizing, and monitoring risks, which are the perfect tasks for machine learning. With training on the huge amount of customer data, financial lending, and insurance results, algorithms can not only increase the risk scoring models but also enhance cost efficiency and sustainability.

Among the most important applications of data science and artificial intelligence (AI) in risk management is identifying the creditworthiness of potential customers. To establish the appropriate credit amount for a particular customer, companies use machine learning algorithms that can analyze past spending behavior and patterns. This approach is also useful while working with new customers or the ones with a brief credit history.

Although digitalization and automatization of risk management processes in finance are in the early stages, the potential is extremely huge. Financial institutions still need to prepare for this change by automating core financial processes, improving analytical skills of the finance team, and making strategic technology investments. But as soon as the company starts to move in this direction, the profit will not make itself wait.

Managing customer data

For financial firms, data is the most important resource. Therefore, efficient data management is a key to business success. Today, there is a massive volume of financial data diversity in structure and volume: from social media activity and mobile interactions to market data and transaction details. Financial specialists often have to work with semi-structured or unstructured data and there is a big challenge to process it manually.

However, it’s obvious for most companies that integrating machine learning techniques to managing process is simply a necessity to extract real intelligence from data. AI tools, in particular, natural language processing, data mining, and text analytics, help to transform data into information contributing in smarter data governance and better business solutions, and as a result – increased profitability. For instance, machine learning algorithms can analyze the influence of some specific financial trends and market developments by learning from customers financial historical data. Finally, these techniques can be used to generate automated reports.

Predictive analytics

Analytics is now at the core of financial services. Special attention deserves predictive analytics that reveals patterns in the data that foresee the future event that can be acted upon now. Through understanding social media, news trends, and other data sources these sophisticated analytics conquered the main applications such as predicting prices and customers lifetime value, future life events, anticipated churn, and the stock market moves. Most importantly such techniques can help answer the complicated question – how best to intervene.

Real-time analytics

Real-time analytics fundamentally transform financial processes by analyzing large amounts of data from different sources and quickly identifying any changes and finding the best reaction to them. There are 3 main directions for real-time analytics application in finance:

Fraud detection

It’s an obligation for financial firms to guarantee the highest level of security to its users. The main challenge for companies is to find a good fraud detecting system with criminals always hacking new ways and setting up new traps. Only qualified data scientists can create perfect algorithms for detection and prevention of any anomalies in user behavior or ongoing working processes in this diversity of frauds. For instance, alerts for unusual financial purchases for a particular user, or large cash withdrawals will lead to blocking those actions, until the customer confirms them. In the stock market, machine learning tools can identify patterns in trading data that might indicate manipulations and alert staff to investigate. However, the greatest thing of such algorithms is the ability of self-teaching, becoming more and more effective and intelligent over time.

Consumer analytics

Real-time analytics also help with better understanding of customers and effective personalization. Sophisticated machine learning algorithms and customer sentiment analysis techniques can generate insights from clients behavior, social media interaction, their feedbacks and opinions and improve personalization and enhance the profit. Since the amount of data is enormously huge, only experienced data scientists can make precise breakdown.

Algorithmic trading

This area probably has the biggest impact from real-time analytics since every second is at stake here. Based on the most recent information from analyzing both traditional and non-traditional data, financial institutions can make real-time beneficial decisions. And because this data is often only valuable for a short time, being competitive in this sector means having the fastest methods of analyzing it.

Another prospective opens when combining real-time and predictive analytics in this area. It used to be a popular practice for financial companies have to hire mathematicians who can develop statistical models and use historical data to create trading algorithms that forecast market opportunities. However, today artificial intelligence offers techniques to make this process faster and what is especially important – constantly improving.

Therefore, data science and AI made a revolution in the trading sector, starting up the algorithmic trading strategies. Most world exchanges use computers that make decisions based on algorithms and correct strategies taking into account new data. Artificial intelligence infinitely processes tons of information, including tweets, financial indicators, data from news and books, and even TV programs. Consequently, it understands today’s worldwide trends and continuously enhances the predictions about financial markets.

All in all, real-time and predictive analytics significantly change the situation in different financial areas. With technologies such as Hadoop, NoSQL and Storm, traditional and non-traditional datasets, and the most precise algorithms, data engineers are changing the way finance used to work.

Deep personalization and customization

Firms realize that one of the key steps to being competitive in today’s market is to raise engagement through high-quality, personalized relationships with their customers. The idea is to analyze digital client experience and modify it taking into account client’s interests and preferences. AI is making significant improvements in understanding human language and emotion, which brings customer personalization to a whole new level. Data engineers can also build models that study the consumers’ behavior and discover situations where customers needed financial advice. The combination of predictive analytic tools and advanced digital delivery options can help with this complicated task, guiding the customer to the best financial solution at the most opportune time and suggesting personalize offerings based on spending habits, social-demographic trends, location, and other preferences.


For financial institutions, the usage of data science techniques provides a huge opportunity to stand out from the competition and reinvent their businesses. There are vast amounts of continuously changing financial data which creates a necessity for engaging machine learning and AI tools into different aspects of the business.

We focused on the top 7 data science use cases in the finance sector in our opinion, but there are many others that also deserve to be mentioned. If you have any further ideas, please share your vision in the comment section.


Shiny vs Useful: Which trends in the analytics market are business ready?


Business analytics continues to be a hot segment in the enterprise software market and a core component of digital transformation for every organization. But there are many specific advances that are at differing points along the continuum of market readiness for actual use.

It is critical that technology leaders recognize the difference between mature trends that can be applied to real-world business scenarios today versus those that are still taking shape but make for awe-inspiring vendor demos. These trends fall into categories ranked from least to most mature in the market: artificial intelligence (AI), natural language processing (NLP), and embedded analytics.

Artificial augments actual human intelligence

The hype and excitement surrounding AI, which encompasses machine learning (ML) and deep learning, has surpassed that of big data in today’s market. The notion of AI completely replacing and automating manual analytical tasks done by humans today is far from application to most real-world use cases. In fact, full automation of analytical workflows should not even be considered the final goal — now or in the future.

The term assistive intelligence is a more appropriate phrase for the AI acronym, and is far more palatable for analysts who view automation as a threat. This concept of assistive intelligence, where analyst or business user skills are augmented by embedded advanced analytic capabilities and machine learning algorithms, is being adopted by a growing number of organizations in the market today. The utility of these types of smart capabilities has proven useful in assisting with data preparation and integration, as well as analytical processes such as the detection of patterns, correlations, outliers and anomalies in data.

Natural interactions improve accessibility of analytics

Natural Language Processing (NLP) and Natural Language Generation (NLG) are often used interchangeably but serve completely different purposes. While both enable natural interactions with analytics platforms, NLP can be thought of as the question-asking part of the equation, whereas NLG is used to render findings and insights in natural language to the user.

Of the two, NLP is more recognizable in the mainstream market as natural language interfaces increasingly become more commonplace in our personal lives through Siri, Cortana, Alexa, Google Home, etc. Analytics vendors are adding NLP functionality into their product offerings to capitalize on this consumer trend and reach a broader range of business users who may find a natural language interface less intimidating than traditional means of analysis. It is inevitable that NLP will become a widely used core component of an analytics platform but it is not currently being utilized across a broad enough range of users or use cases to be considered mainstream in today’s market.

On the other hand, NLG has been in the market for several years but only recently has it been incorporated into mainstream analytics tools to augment the visual representation of data. Many text-based summaries of sporting events, player statistics, mutual fund performance, etc., are created automatically using NLG technology. Increasingly, NLG capabilities are also being used as the delivery mechanism to make AI-based output more consumable to mainstream users.

Recently, analytics vendors have been forging partnerships with NLG vendors to leverage their expertise in adding another dimension to data visualization, where key insights are automatically identified and expressed in a natural language narrative to accompany the visualization. While the combination of business analytics and NLG is relatively new, it is gaining awareness and traction in the market and has opened the door to new uses cases for organizations to explore.

Embedded analytics brings insights closer to action

The true value of analytics is realized when insights can inform decision-making to improve business outcomes. By embedding analytics into applications and systems, where decision-makers conduct normal business, a barrier to adoption is removed and insights are delivered directly to the person who can take immediate action.

Modern analytics platform vendors have made it incredibly easy for organizations to adopt an embedded strategy to proliferate analytic content to line-of-business users previously unreachable by traditional means. And organizations are now extending similar capabilities to customers, partners, suppliers, etc., in an effort to increase competitive differentiation and, in some cases, new revenue streams through monetization of data assets and analytic applications.

These innovations present technology leaders with a unique opportunity to lead their organizations into an era where data analysis is the foundation for all business decisions. Every organization will embark on this journey at its own pace. Some will be early adopters of new innovations and some will only adopt when the majority of the market has successfully implemented.

Ultimately, organizational readiness to adopt any new technology will be determined by end users and their ability and willingness to adopt new innovations and embrace process change.

Source: Tableau

Human work starts where analytics stops

It has been a while since the hype, and now it’s time for some reality check.

Everyone has been riding on the data wave and analytics has been the biggest buzz word for almost a decade now.

Let us talk about the impact, success and ROI now.
◾How many companies do you think are really changing the way they have been , by adopting analytics?
◾How many businesses are data driven? In real sense?
◾How many enterprises are running at scale with big data and Machine learning algorithms driving decision making?
◾How many data scientist are proven to be different than a BI developer ?
◾How many dollars are being saved because every decision you make in business is driven by some data sense?
◾How many companies have developed a culture of analytics and data driven thinking, elevating their position in the competitive game?

While Gartner recently emphasized on the fact how BI and analytics is shifting to a decentralized, non-IT centric and self –service approach, I think there is still a huge gap in what “Actual Value” from analytics is vs. the “perceived value”

Here are some of the factors I think need to be pondered over to bridge the gap, and which is more of a human change than vouching for machines to achieve this.

Positioning Analytics in right sense, and creating champions for driving the change

It is extremely important to position analytics in alignment with the objectives of the organization. The organizations need to be firm on the “need for data driven thinking”. Analytics is not a “good-to-have” function, it is must-to-have. A dollar saved in a dollar earned. An opportunity unexplored is 100% incremental. Especially, if you are starting or have recently started with on-boarding an analytics team or a consulting company, you need to do a PR for the “Expected Value” by having an analytics team in place. There needs to be people in the company who take accountability for helping analytics position itself as “in demand” and “Risk mitigator”

Embedding analytics in the strategy, than keeping analytics as a separate strategy

Most of the companies keep analytics as a separate strategy, the goal is to build an analytics infrastructure, team and so to say practice. That’s a traditional IT approach. It is only partially about systems, it is much more about education and integration. Analytics will not work on it’s own. There has to be someone who should act on what data and algorithms find out, and at the same speed. There need to accountability for every single insight to be worked upon. If not, There are always new problems to solve, and the problems too get old and redundant.

Democratization of data as “capability” vs “availability”

There has been great talks about how analytics is moving to be self-service where business owners are taking data into their own hands and marketing technology is enabling marketers to be data-savvy. This whole propaganda is about availability. I bet, no one is looking at this as capability enchantment. Is the self-service realization making marketers smarter and efficient? Or it is just that they do not have to depend on IT to get some data? Think

Creating room for “change in business” ideas vs “Business as usual” ideas

Innovation has always been the escape word. If you do not have a strategy , just tell them you are working on innovation. Now, innovation is extremely debatable in its definition. When talked about results from investment on innovation, an easy answer is – Innovation is not only about creating something new, it is also about improving the existing stuff. Well, I think this needs to stricter than it is now. You have to have a dedicated team with dedicated time working to change things and innovate using analytics and data. Process improvement with data is a low value fruit, the real value is in disruption and in pivoting strategies with data

Introducing constraints for Optimization, than perceiving improvement as Optimization

I have rarely met anyone who takes “Optimization” in the right spirit. Usually, people perceive improvement as optimization. Optimization is about pushing the boundaries. It is about putting constraints on your targets and over achieve. If you want to Optimize your digital channel, for example, you need to introduce constraints around cost, exposure and spend around the same and then push for maximizing the output. Analytics is a great tool to drive Optimization and that’s where it is different from traditional BI.

So, if you think you are already doing great with data, it is time to introspect a bit.


How Data Analytics Make Airlines Fly


Look up at the sky and think in numbers: Each day, nearly 7,000 commercial aircraft take off on 24,000 flights, according to the Federal Aviation Administration. With the expert guidance of 14,000 air traffic controllers, they fly about 2.2 million passengers every 24 hours.

To do that, airlines and controllers coordinate airplanes’ movements through internal dispatch offices and 476 control towers. Add to the mix some 200,000 general aviation aircraft traveling between more than 19,000 airports, and you can envision the logistical challenges involved in moving 719 million passengers around the U.S. each year.

It doesn’t take much to imagine the complex logistics involved in making a system of such scale work properly. While “data analytics” is a relatively new term to the mainstream, airlines have been applying such principles to the business for years, often under the phrase “operations modeling,” said Doug Gray, director of enterprise data analytics for Dallas-based Southwest Airlines.

At first, the data was used to guide decisions about fueling, crew schedules and flight itineraries. Today, analytics are used across the organization, in functions from marketing to operations, explained Gray, who’s also a member of the International Institute for Analytics’ Expert Network. “They have a significant impact on costs and profitability,” he said.

Indeed, calling real-time analytics “integral” to any airline’s success isn’t going too far. While specialists in fueling, crew scheduling, flight scheduling and other areas may have their plans laid out perfectly, their work is never immune from unexpected developments.

“Daily operations is where stuff hits the fan,” Gray observed. “You can’t predict a heart attack in-flight, or a control-tower fire.” Schedulers have only so much advance warning of bad weather. Even on the best of days, it seems an airline’s plans can only be regarded as tentative.

Real-Time Analytics to Face Real-Time Challenges
To help things move as smoothly as possible under almost any circumstances, Southwest launched “the Regular Operations Recovery Optimizer,” a system that takes an incident’s consequences, factors them into the airline’s operations at that moment, and “rejiggers events on-the-fly,” Gray said. The first of its kind in the industry, the Optimizer’s ability to crunch data and quickly propose solutions may give Southwest “a unique competitive advantage.”

With such systems in place, and the use of data only expected to grow, it’s no surprise that Gray predicts a constant demand for the right technical talent within the industry. But as he noted, analytics systems rely on more than data specialists to make them work. While he oversees an organization of some 200 employees, only about 10 percent hold a data scientist role or similar positions. The others are experts in areas such as data warehousing and ETL (Extract, Transform, Load), and still others have expertise in some of the airline’s nuts-and-bolts functions, such as the fuel supply chain.

“We look for hardcore operations research people who can work closely with domain experts,” Gray said. Though Southwest recruits from the industry, it also seeks newly minted masters of science in data analytics from nearby schools such as the University of Texas at Austin and Southern Methodist University.

Industry Expertise Helps
Of course, airlines need people who can implement and maintain analytics systems—software engineers and developers who need to have at least some expertise with data, Gray said.

For his part, Gray is particularly interested in tech pros familiar with Oracle, Teradata and Amazon Cloud Services. Also important are relational database skills such as NoSQL and Mongo DB. Although he sees the company experimenting more with Hadoop (“It’s better in the cloud,”), Gray said that “unless something better comes along,” the company will continue to rely heavily on R and Alteryx, a “data science self-service desktop” that combines ETL, R and visualization in one GUI-driven application.

Like many employers, Southwest will hire “the right person and train them” on needed skills, especially if they’re just out of school, Gray said. And, he believes, industry experience can give candidates an advantage. As more people pursue careers using data science, analytics and operations research, “more departments will have their own [data and analytics] experts, joined together by a center of excellence.”

“Our biggest constraint isn’t data,” he added. “It’s teaming up the right data people with the right subject-matter experts. There’s going to be a battle for talent, and we need people with a passion for the airline business.”


How to build a high performing analytics team?


Having BI capabilities have proven to help organizations improve efficiency and stay ahead in overall competitiveness.  With digital transformation taking over sectors, businesses continue to evolve into data driven models. Each day more businesses are focusing on insights from data that drive better decisions and strengthen customer relationships.

Self-service tools such as Tableau, Alteryx, Qlikview, Power BI etc. have introduced a dash of ease into ways businesses convert their data into insights, making business intelligence (BI) initiatives go mainstream.

Choosing analytics providers, structuring an effective BI ecosystem is easy. The difficulty that decision makers face is building a good quality BI team. In the light of this talent crisis, an accelerating number of companies are motivated to hire any resource they can get, but those approaches mostly go dysfunctional. This not only incurs excessive cost burden for businesses but also makes room of inefficiency leading to the failure of BI endeavors.

Some challenges enterprises face while building BI teams:
•Lack of efficient analytics workforce makes way for inevitable competitive lag.
•It is tough for organizations to fulfil skill development needs of their large teams.
•Technical resources are masters of tools but lack the art of business storytelling with analytics.
•Hiring specific SMEs for varied functions of the BI lifecycle drains budget and keeps ROIs in doubt.
•Businesses waste time over manpower fulfilment when they could be leveraging a BI solution for business gains.
•Longer learning curves because of the diversity of tools and the stern need for accuracy in BI ecosystems
•No accountability for the inevitable technical or team related roadblocks that can appear amidst a BI lifecycle.

The options
Every business has its unique demands when it comes to building a data workforce. A different approach is needed for each instance and there are several ways, to begin with. The first step being gauging the magnitude of the BI initiative and the expectations from it. Once that’s in place businesses can start to make decisions whether to hire, outsource or augment.

Outsource – If it is right for you
Organizations leveraging data for certain projects or for improvements of business processes need not go that way. If BI is a support initiative, partnering with third parties to execute analytics initiatives is the best bet.

Having a full-time in-house analytics team an attractive proposition but is expensive to manage and especially when companies are new to BI and unsure of ROIs. Outsourcing is an easier, faster and cheaper way to jumpstart analytics endeavors for such businesses.

In-house teams – build and improve over time
Large enterprises having data and analytics as their core business strategy need to put all BI components i.e. people, processes, and platforms in place. Certain business models cannot allow data to seep out of the organization? Other than those reasons, if businesses know that once rolled out BI initiatives will not be taken back, they can go ahead and start hiring specialists to build in-house BI teams.

In-house data teams can determine and control data lineage, but how do businesses ensure a perfect team that is capable of translating data into success? The answer is – if candidates have the aptitude for analytics and know a tool well in the BI arena, they are potentially capable of evolving as BI needs of businesses do and make great hires that cannot be easily poached. The major takeaway here is hiring people who are masters of their tool and constantly provide opportunities to learn and grow so that the team is up to date, always.

Extended data teams – The best model so far
Finding a qualified match for any role in the IT industry is not an easy job but with the diversity of tools, processes, and evolution involved in BI presents some unique roadblocks in hiring the right talent for BI endeavors.

There’s a lot of maturation time in hiring an internal team and training them to attain optimum results from a BI initiative. An extended team can help chart a course through BI endeavors, with a flexibility that these teams can be involved at any stage of the BI lifecycle.

Extended teams came into the picture to free businesses of unruly time and monetary investments. They also eliminate the worries of hiring, training, and fear of losing seasoned BI experts. The best part is that as a customer you can call off the engagement, the minute the projects starts to derail, saving cost and time resources


The Vast, Secretive Face Database That Could Instantly ID You In A Crowd

Amid questions of accuracy and legality, the FBI and police can search images for millions of Americans’ faces, potentially in real time.


If you’ve been to the DMV to have your driver’s license photo taken, there’s a good chance your face is in a little-known group of databases that functions like a digital police lineup. Except, in this case, you needn’t even be suspected of a crime, and soon the searching could take place virtually anywhere, anytime.

It’s just one example of the exploding use of facial recognition technology in law enforcement, and it’s raising major concerns among U.S. lawmakers and privacy advocates.

“I’m frankly appalled,” Representative Paul Mitchell, a Republican from Michigan, told Kimberly Del Greco, the FBI’s deputy assistant director of criminal justice, during a House oversight committee hearing last week. “I wasn’t informed when my driver’s license was renewed my photograph was going to be in a repository that could be searched by law enforcement across the country.”

Mitchell’s face and those of more than 125 million Americans—more than half of the country’s adult population—are thought to be stored in a vast network of databases used by local and federal law enforcement to scan photos and videos of individuals. Many of these faces, which can be searched without reasonable suspicion, belong to people who have never been charged with a crime and have no idea they are being searched.

Yet there are few guidelines or legal rulings that govern exactly how face recognition, like a number of other new technologies, should be used by police. In a report last May on the FBI’s system, the Government Accountability Office (GAO) found the FBI had “not fully adhered to privacy laws and policies and had not taken sufficient action to help ensure accuracy of its face recognition technology.” In response to the report, which urged the FBI to conduct regular audits of the system, “the Department of Justice and the FBI disagreed with three recommendations and had taken some actions to address the remainder, but had not fully implemented them,” the GAO said.

“No federal law controls this technology, no court decision limits it,” said Alvaro Bedoya, executive director of Georgetown Law’s Center on Privacy and Technology, and the coauthor of “The Perpetual Lineup,” a report on the FBI and state face recognition databases. “This technology is not under control.”

While a few attempts to set limits are inching slowly through state legislatures, the technology is racing ahead. Advancements in machine vision and artificial intelligence are widening the scope of the lineup too: Via body-worn police cameras, which are rapidly proliferating, face searches could happen up-close, at street level and in real-time—anticipating a future in which anonymity in certain public places could disappear.

It’s this pairing of technologies in particular—the ability to scan and identify faces on the street—that is the “most concerning” from a privacy and First Amendment perspective, said Jason Chaffetz, Republican representative from Utah and chairman of the House Oversight committee. A 2014 Justice Dept. report also highlighted the combination, warning that using body cameras with “facial recognition systems and other new technologies like live feed and auto recording . . . may pose serious risks to public privacy.” In the legal vacuum surrounding their use, agencies exploring these technologies should “proceed very cautiously,” the report said.

“Imagine the world where the cops are going down the street and they’ve got Google Glass on and their body cameras are recognizing people,” says Barry Friedman, the director of the Policing Project at New York University School of Law. “And it’s not just recognizing them, but they’re getting their security scores at the same time, and people are getting colored based on how dangerous the algorithms think they are. That’s one scary world.”

Already law enforcement is pairing real-time face recognition software with footage from surveillance cameras, and police officers around the country are using face recognition apps on mobile phones to more quickly identify suspects they stop on the street. In New York, it emerged this week that police are beginning to acquire face recognition technology to scan the faces of all drivers commuting between the five boroughs.

Questions Over Legality And Accuracy

Of immediate concern to Congress was the legality of the FBI’s system. With the FBI’s Del Greco in the hot seat, a number of committee members asked why the agency hadn’t published a privacy report of its face recognition system until 2015, years after it first deployed the technology in public, in 2010.

Had anyone at the FBI been reprimanded for the delay? Rep. Mitchell asked.

“I have no knowledge,” Del Greco said. “There are days ignorance is bliss,” he fired back.

Del Greco said the FBI had advised its privacy attorney internally throughout the roll-out of the system.

“We don’t believe you,” Chaffetz said, “and you’re supposed to make it public.” He also alleged that the FBI “went out of its way” to exempt its facial recognition database from the Privacy Act.

“So here’s the problem,” said Chaffetz. “You’re required by law to put out a privacy statement and you didn’t and now we’re supposed to trust you with hundreds of millions of people’s faces.”


Del Greco defended the agency’s use of what she referred to as “face services,” saying it had “enhanced the ability to solve crime,” emphasized that privacy was of utmost importance at the FBI, and said that the system was not used to positively identify suspects, but to generate “investigative leads.” In one recent positive outcome for the technology, Charles Hollin, an alleged child molester, was caught after spending 18 years as a fugitive, thanks to a database that contained his passport photo.

Currently, 18 U.S. states let the FBI use face-recognition technology to compare suspected criminals to their driver’s license or other ID photos. These, in addition to criminal and civil mug shots, and photos from the U.S. State Department’s passport and visa records, are part of a set of databases used by the FBI and police across the country. Over the past decade and a half, 29 states have allowed police agencies and the FBI to search their repositories of drivers’ faces during investigations.

“I have zero confidence in the FBI and the [Justice Department], frankly, to keep this in check,” Rep. Stephen Lynch, a Democrat from Massachusetts, said.

After the hearing, Bedoya, a former chief counsel for Sen. Al Franken of Minnesota, noted that the discussion had inspired a rare moment of bipartisan agreement. “The opposition to the use of driver’s licenses was remarkably strong and remarkably uniform across party lines,” he said. “In my five years as a Senate staffer, I never saw anything like it on a similar privacy issue.”

In some ways, the FBI’s approach to face recognition resembles how police traditionally try to match fingerprints at a crime scene to those of criminals. In this case, however, the “prints” include the faces of millions of innocent people, often collected and scanned without their knowledge, according to the analysis by Bedoya and his colleagues. It estimated that around 80% of the faces searchable by the FBI belong to individuals who have never been charged with a crime.

In under half a decade, they found, the FBI searched drivers’ faces more than 36,000 times, without warrants, audits, or regular accuracy tests. In Florida, police are encouraged to use face recognition “whenever practical.” (Individually, dozens of states also use face recognition to crack down on fraudsters applying for duplicate driver’s licenses, for instance.)

To access the FBI’s networked databases of faces, an authorized, participating police agency need only show that its search is for “law enforcement purposes,” said Del Greco of the FBI. Those criteria are determined by individual states, she noted.

Imagine going to the DMV to renew your license, Bedoya wrote in a recent op-ed in the Washington Post. “What if you—and most other teens in the United States—were then asked to submit your fingerprints for criminal investigations by the FBI or state police?”

In scope, he argues, the face-matching system resembles the National Security Agency’s call-records program, which logged the metadata of all Americans’ phone calls. “This has never happened before—not with DNA or fingerprints, which are kept in smaller national networks made up mostly of known or suspected criminals. Yet law-enforcement face-recognition systems have received a fraction of the NSA’s oversight.”

The possibility of misidentification and false positives is also worrisome, especially because the FBI has not been keeping track of such failures. “It doesn’t know how often the system incorrectly identifies the wrong subject,” explained the GAO’s Diana Maurer. “Innocent people could bear the burden of being falsely accused, including the implication of having federal investigators turn up at their home or business.”

More worrisome, research shows that facial recognition appears to disproportionately impact minority communities. A report that was co-written by the FBI in 2012 found that the technology exhibited a higher number of failure rates with darker faces, a function of the kinds of data that humans input as they train the algorithms.

“If you are black, you are more likely to be subjected to this technology, and the technology is more likely to be wrong,” said Elijah Cummings, a congressman for Maryland. “That’s one hell of a combination. Just let that sink in.”

The FBI, like other law enforcement agencies, has argued that the algorithms are race-blind, and reiterates that face searches are only used as “investigatory” leads. “This response is very troubling,” Cummings noted. “Rather than conducting testing that would show whether or not these concerns have merit, the FBI chooses to ignore growing evidence that the technology has a disproportionate impact on African Americans.”

Friedman notes that racial concerns aren’t exclusive to face recognition. “A lot of these technologies, just because of how they’re deployed, come with racialized aspects. If you’re using license plate readers in more heavily policed neighborhoods, you’re picking up more data in those neighborhoods. That’s something we need to be very aware of and thoughtful about.”


A plethora of private companies already have your face in all of its biometric glory, provided it’s ever been uploaded to the servers of firms like Apple, Google, and Facebook. As these companies pile millions into AI research—in part, to better find you and objects in photos—a number of startups are also racing to automatically analyze the world’s video. One startup, Kairos, aims to let Hollywood and ad agencies study audiences’ emotional responses and help theme park visitors more easily find and purchase photos of themselves. Another, Matroid, launched this week by researchers at Stanford, focuses on analyzing television appearances and scanning surveillance video.

“Google can give you pictures of cats, but not cat with grandpa or cat with grandpa and Christmas tree or with your son,” Pete Sonsini, a general partner at New Enterprise Associates, which is funding Matroid to an unspecified tune, told Bloomberg. “It’s really powerful for any human to be able to create a detector that can identify any image or set of images or face from their dataset.”

Banks are using biometric technology to provide better personal verification, eventually allowing people to pay with their face. Airlines are imagining using biometrics to let passengers board an airline without a paper ticket. At a conference this week in Orlando, NEC, the Japanese company whose facial recognition algorithms are considered the most accurate by the Dept. of Homeland Security, released new features for its software suite which include a “virtual receptionist” and a system by which “age/gender recognition can trigger tailored advertisements/greetings and can trigger notifications to sales personnel for immediate follow-up and interaction,” said a press release.

Another app, installed on a hotel’s cameras, would notify hotel receptionist and concierge services when a VIP or high value customer arrives at any of the building’s entrances. “This will allow them to receive them in person, greet them by their name, and provide them a better service. Also this can be used to identify any staff that were terminated when they enter the property and take appropriate action.”

At Beijing’s Temple of Heaven Park, meanwhile, biometrics are already being used to ration toilet paper, CNN reported last week: “The facial recognition program keeps the dispenser from offering another round to the same person; if you need more, you reportedly need to sit tight—literally—for nine minutes.”

Still, the most profitable applications for biometrics lie in the fast-growing law enforcement and public safety sector. Industry executives and police experts say that the next stage of the technology—automatically scanning public places for faces and objects in real time—could help more quickly find armed and dangerous or missing persons, identify critical moments amid torrents of body and dash cam footage, or even perhaps identify biased policing.

In many communities, automatic license plate readers—brick-sized cameras mounted on the back of patrol cars—already do something similar with cars, archiving and cross-referencing every license plate they pass in real time to check for outstanding warrants or traffic violations.

These are also the most worrisome applications, privacy advocates say, given the general secrecy that surrounds them, the few restrictions on their use, and the ability to track individuals. Pairing face-matching algorithms with body cameras, said Bedoya, “will redefine the nature of public spaces.”

Even the mere prospect of the technology could have a chilling effect on people’s First Amendment rights, privacy advocates warn. In some cities, police are restricted from filming at protests and demonstrations—unless a crime is thought to be in progress—for this reason.

Other police departments, however, have routinely filmed protesters: In New York, for instance, the NYPD sent video teams to record Occupy and Black Lives Matter protests hundreds of times, and apparently without proper authorization, agency documents released this week show.

“Will you attend a protest if you know the government can secretly scan your face and identify you—as police in Baltimore did during the Freddie Gray protests?” Bedoya writes. “Do you have the right to walk down your street without having your face scanned? If you don’t, will you lead your life in the same way? Will you go to a psychiatrist? A marriage counselor? An Alcoholics Anonymous meeting?”

Rep. Chaffetz appeared to support one controversial application of face recognition: using it to find undocumented immigrants. The Dept. of Homeland Security stores the faces of every visitor in its own database, and is determined to better track those who overstay their visas; in 2015, the agency estimated 500,000 overstays.

“I think it is absolutely a concern that face recognition would be used to facilitate deportations,” Rachel Levinson-Waldman, senior counsel to the Brennan Center’s National Security Program at New York University School of Law, told The Intercept‘s Ava Kofman. “We’re seeing how this administration is ramping up these deportation efforts. They’re looking much more widely.”

Generally, however, Chaffetz urged firmer limits. The technology “can also be used by bad actors to harass or stalk individuals,” he said. “It can be used in a way that chills free speech and free association by targeting people attending certain political meetings, protests, churches, or other types of places in the public.”

“And then having a system, with a network of cameras, where you go out in public, that too can be collected. And then used in the wrong hands, nefarious hands… it does scare me. Are you aware of any other country that does this? Anybody on this panel? Anybody else doing this?”

Neither Del Greco nor other members of the panel responded.

Jennifer Lynch, a staff attorney at the Electronic Frontier Foundation, noted that “we don’t yet appear to be at point where face recognition is being used broadly to monitor the public.” But, she said, “it is important to place meaningful checks on government use of face recognition now before we reach a point of no return.”

Real-time face recognition is coming to law enforcement; the question is how the technology itself will be policed, and what role the public will have in dictating its use. “If we’re going there, then we better be going there together,” said Friedman, of the Policing Project. “Did we all discuss this and agree to this?”

To improve the use of face recognition, Bedoya and other privacy advocates urge well-defined limits. The FBI should only access mugshot databases with reasonable suspicion of a crime, and, if it’s a forensic use—a face recognition scan on surveillance footage, for instance, as opposed to a police officer photographing a person he has stopped—the technology should only be used in cases of felonies.

For databases containing drivers license photos, Bedoya says law enforcement should have express consent from state legislatures, and only search those databases when they have probable cause to implicate the subject for a serious offense, like the kind required for a wiretap. The FBI should also regularly scrub its databases to remove innocent people, be more transparent about how it uses the technology, and, as the GAO recommended, conduct audits to ensure the software meets privacy and accuracy requirements.

Policing veterans have also expressed discomfort with the use of the technology. In December, a guide to body cameras published by the Constitution Project, a nonpartisan think tank, and co-authored by a handful of retired police officials, warned that the privacy risks of video “tagging” technologies were “immense” because they “have the potential to end anonymity, catalog every person at a sensitive location or event, and even facilitate pervasive location tracking of an individual over a prolonged period of time.”

In an interview about the future of body cameras, Bill Schrier, the former chief information officer at the Seattle Police Department and a 30-year veteran of government technology, told me that “most reasonable people don’t want potentially dangerous felons or sex offenders walking around in public and would, I think, support such use” of real-time face recognizing body cameras in those cases.

But used to catch people wanted for “misdemeanors such as unpaid traffic tickets or pot smoking,” real-time face recognition could be dangerous. That, he said, could “seriously undermine faith in government, and start us down the road to a police state.”

Source: FastCompany

Robotic Process Automation + Analytics

“Looking to the future, the next big step will be for the very concept of the “device” to fade away. Over time, the computer itself—whatever its form factor—will be an intelligent assistant helping you through your day. We will move from mobile first to an AI first world.” — Sundar Pichai, CEO Google


•A global oil and gas company has trained software robots to help provide a prompt and more efficient way of answering invoicing queries from its suppliers.
•A large US-based media services organization taught software robots how to support first line agents in order to raise the bar for customer service.

Software agents or Robotic process automation (RPA) is becoming a mainstream topic at leading corporations. I have seen a massive uptick in corporate strategy work in this area as C-Suite execs look at new ways to do more with less.

Software robots ∼ Conversational-AI products like Apple Siri, Microsoft Cortana, IBM Watson, Google Home, Alexa, drones and driverless cars ∼ are now mainstream. What most people are not aware of is the rapidly advancing area of enterprise robots to create a “virtual FTE  workforce” and transform business processes by enabling automation of manual, rules based, back office administrative processes.

This emerging process re-engineering area is called Robotic process automation (RPA).

Machine Learning (ML) and graph processing are becoming foundations for the next wave of advanced analytics use cases. Speech recognition, image processing, language translation have gone from a demo tech to everyday use in part because of machine learning. Machine learning models, e.g., in driverless cars,  teaches itself how to discover relevant things like a stop sign with snow partially obscuring the sign.

The market opportunity of artificial intelligence has been expanding rapidly, with analyst firm IDC predicting that the worldwide content analytics, discovery and cognitive systems software market will grow from US$4.5 billion in 2014 to US$9.2 billion in 2019, with others citing these systems as catalyst to have a US$5 trillion – US$7 trillion potential economic impact by 2025.

RPA – What?
“Robotic automation refers to a style of automation where a machine, or computer, mimics a human’s action in completing rules based tasks.” – Blue Prism

RPA is the application of analytics, machine learning and rules based software to capture and interpret existing data input streams for processing a transaction, manipulating data, triggering responses and driving business process automation around enterprise applications (ERP, HRMS, SCM, SFA, CRM etc.).

RPA is not a question of “if” anymore but a question of “when.”  This is truly the next frontier of business process automation, enterprise cognitive computing, predictive analytics and machine learning. To make a prediction, you need an equation and parameters that might be involved.

Industrial robots are remaking blue-collar factory and warehouse automation by creating higher production rates and improved quality.  RPA, simple robots and complex learning robots, are revolutionizing white-collar business processes (e.g. customer service), workflow processes (e.g., order to cash), IT support processes (e.g., auditing and monitoring), and back-office work (e.g., data entry).

I strongly believe that as cognitive computing slowly but surely takes off, RPA is going to impact process outsourcers (e.g., call center agents) and labor intensive white collar jobs (e.g., compliance monitoring) in a big way over the next decade. Any company that uses labor on a large scale for general knowledge process work, where workers are performing high-volume, highly transactional process functions, will save money and time with robotic process automation software.