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A paradigm shift

graphical_viewIs it possible that Spotify knows our music taste better than we do? Every week, Spotify offers us a custom playlist, called “Discover Weekly”, of 30 songs that we might never listened to before, but we shall probably like. When we think about it, the ability to predict our preferences is the secret sauce behind the world’s greatest data companies such as Netflix, Google, Amazon and Facebook. And if they know us so well, why should other service providers who accompany our life journey, such as Banks and Insurers be any different?

As personalization is becoming part of our everyday life, people are becoming more open to the idea of sharing their data and service providers are learning how to make the best of such personal data. In a recent report, Accenture is stating that nearly 80% of the insurance customers are willing to share their personal data in exchange for lower premiums or other benefits such as insurance coverage recommendations. Consequently, Insurance carriers are increasingly looking for innovative solutions to personalize their offering, messaging and pricing. After a decade during which only the data rails owners (Google, Facebook, etc.) mastered and leveraged personal data to increase conversion, the signs are indicating that financial service providers are waking up and attempt to bridge the knowledge gap by using deep personalization technologies to feed AI systems and improve the performance of their newly deployed digital channels.

“PERSONA” is key for Insuretech

graphical_viewWhile the life insurance market size is estimated to reach $2.8T by 2030 (Munich Re), insurance customers are still very costly  to acquire – the average Customer Acquisition Cost (CAC) in this space ranges between $500-$800 and characterized by users that are hard to convert. During 2018 for example, although half of all adults in the US have searched on-line for life-insurance information, only 1 in 3 purchased or attempted to purchase a policy. This fact is not surprising given that most insurers websites are still designed to offer a “serve all” experience, with a “fix” set of solutions.

A study by PWC, shows that the ‘serve all’ model is not relevant anymore and the only way for insurers to acquire customers at a reasonable CAC, is by (i) understanding in real-time who the customer is (even if that is his first entry to the portal) and (ii) based on pre-configured customer segments, offering a differentiated digital journey and products to each “Persona”.

The underlying technology

The personalization paradigm is comprised of two separate challenges (i) Converting new users that were never engaged before; (ii) Selling more, to existing customers.  Both domains require very deep understanding of the customers, but in completely different ways.

Aktibo, our recent investee startup is dealing with the first challenge. Bringing vast knowledge from the digital marketing space, Aktibo enables insurers to classify in real time every user who enters the digital channel (portal/App) and personalize the channel and the offering specially for this user. Tests of the technology with large US’ insurance carriers resulted in 300% more conversions of surfers into buyers.

Neura, another portfolio startup in which Moneta has recently invested, is dealing with the 2nd challenge: It is learning the real-life habits and behaviors of existing customers and allow brands to engage with them at the right time and place, or in other words – when it matters most for these clients.

What these hypotheses mean in reality, is that a high-tech parent in his/her thirties will enter a completely different portal (look-feel-colors), see different products, and read different messages, than a millennial student (or a teacher in his/her fifties). The offering that matches their “Persona” is a result of deep learning that is based on millions of prior cases learned by computers (Machina Leaning) which suggested the offering that will most likely be favored by the specific “Persona”.

graphical_viewThe use of deep personalization technology is expected to keep growing and become a game changer for digital service providers. Definitions of Personas are enhanced all the time by additional data gathered by the systems. Then, feedback models, based on personas, are used to identify and attract the “high value customers”. Adding the ability to monitor customers’ behavior and follow their lifestyle in the real world (i.e. give score to Life Insurance customers who maintain healthy lifestyle) open an opportunity for service providers to incentivize their customers to adopt a desired lifestyle (i.e. by giving discounts) and to engage the customers with new offers at a moment that works best for the them.


With high acquisition costs and an increasing competition from tech companies, insurers can no longer leg-behind the digital revolution. Customers are craving for personalized offers and experiences. Technologies such as Neura and Aktibo are already there to help major carriers adopt to the digital era. BCG research is suggesting that insurance companies that will be smart and agile enough to move quickly to personalization are predicted to enjoy revenue increase by 6% to 10%, two to three times faster than those that won’t.


Moneta Capital First Closing English Version

Your Company Collect Tons of Data: Now What? Data can bring huge valve to business, but simply

graphical_viewData and analytic technologies are shaking up the world and the effects are poised to reshuffle the landscape in multiple industries. In a lecture at the Google user’s meeting in 2006, Steve Jobs, Founder and CEO of Apple, used this phrase to emphasize the tectonic change in consumer’s habits:“if your user is not the one paying, then he is not the customer but rather the product being sold”. These words reflecting on the huge value of data, seem more relevant now than ever before: users’ data is becoming the core value being sold.

(Part of this writing is Sourced from Mckinsey & Co. Dec. 2016)

Data and analytics capabilities have made a leap forward in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. The convergence of these trends is fueling rapid technology advances and business disruptions.

Data is now a critical corporate asset. It comes from the web, billions of phones, sensors, payment systems, cameras, and a huge array of other sources—and its value is tied to its ultimate use. While data itself will become increasingly commoditized, value is likely to accrue to the owners of scarce data, to players that aggregate data in unique ways, and especially to providers of valuable analytics.

machinelearningRecent advances in machine learning can be used to solve a tremendous variety of problems—and deep learning is pushing the boundaries even further. Systems enabled by machine learning can provide customer service, manage logistics, analyze medical records, or even write news stories. The value potential is everywhere, even in industries that have been slow to digitize. These technologies could generate productivity gains and an improved quality of life—along with job losses and other disruptions.


Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass. An even bigger wave of change is looming on the horizon as deep learning reaches maturity, giving machines unprecedented capabilities to think, problem-solve, and understand language. Organizations that areable to harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage.

Analytics leaders are changing the nature of competition and consolidating big advantages. There are now major disparities in performance between a small group of technology leaders and the average company—in some cases creating winner-take-most dynamics. Leaders such as Apple, Alphabet/Google, Amazon, Facebook, Microsoft, GE, and Alibaba Group have established themselves as some of the most valuable companies in the world. The same trend can be seen among privately held companies. The leading global “unicorns” tend to be companies with business models predicated on data and analytics, such as:Uber, Lyft, Didi Chuxing, Palantir, Flipkart, Airbnb, DJI, Snapchat, Pinterest, BlaBlaCar, and Spotify. These companies differentiate themselves through their data and analytics assets, processes, and strategies.

The relative value of various assets has shifted. Where previous titans of industry poured billions into factories and equipment, the new leaders invest heavily in digital platforms, data, and analytical talent. New digital native players can circumvent traditional barriers to entry, such as the need to build traditional fixed assets, which enables them to enter markets with surprising speed. Amazon challenged the rest of the retail sector without building stores (though it does have a highly digitized physical distribution network), “fintechs” are providing financial services without physical bank branches, Netflix is changing the media landscape without connecting cables to customers’ homes, and Airbnb has introduced a radical new model in the hospitality sector without building hotels.

According to Forbes magazine May2016, the Banking, Financial services & Insurance sectors accounted for 25% of the Data driven transformation.

The value of data and analytics has upended the traditional relationship between consumers and producers. In the past, companies sold products to their customers in return for money and negligible data. Today, transactions—and indeed every interaction with a consumer— produce valuable information. Sometimes the data itself is so valuable that companies such as Facebook, LinkedIn, Pinterest, Twitter, and many others are willing to offer free services in order to obtain it. In some cases, the “customer” is actually a user who barters his or her data in exchange for free use of the product or service. fintech In others, the actual customers may be marketers that pay for targeted advertising based on user-generated data. To maintain an edge in consumer data, user acquisition and user interaction are both critical. Venture capitalists have long understood the importance of building a customer base. Many internet startups from Quora to Jet have focused as much attention on capturing users who can provide valuable data as on capturing paying customers.

startupThe data driven technologies are allowing new players to challenge incumbents with surprising speed since they circumvent the need to build traditional fixed assets. Amazon, Netflix, Uber, Airbnb, and a host of new “fintech” financial firms have moved into industries where incumbents were heavily invested in certain types of physical assets. These disrupters used their digital, data, and analytics assets to create value without owning physical shops, cable connections to viewers’ homes, car fleets, hotels, or bank branches, respectively.

Retail banking is ripe for massive data integration to change the nature of competition

Retail banking has always been data-rich, with stores of information on customers’ transactions, financial status, and demographics. But few institutions have fully taken advantage of this data due to internal barriers that limit access across the organization, the variable quality of the data, and sometimes even an inability to see the value that data could generate.Surmounting these barriers is becoming critical now that a plethora of new data sources can be added to existing transaction records. These include social media posts, call center discussions, video footage from branches, and data acquired from external sources and partners. In addition, retail banks can partner with mobile phone operators and retailers to complement their view of each customer.Adding analytics on top of all the data can enhance the customer experience, enabling banks to retain existing customers and attract new ones. Customers increasingly expect a personalized experience across all channels.

The banks also want banking services to be available on their preferred platforms; a growing number are making payments directly via messaging apps, for instance. Integration tools such as data lakes help to provide a holistic view of each customer by combining different sociodemographic and behavioral data. One of the important applications for banks is the ability to improve risk and credit assessments by drawing on data from multiple sources for more accurate predictions.

Massive data integration repositories and the analytics they enable can also optimize banking operations and increase revenue (See Exhibit below). This is critical in an era when low interest rates are putting pressure on margins, regulatory and reporting requirements are growing more complex, and new digital native startups are introducing innovative business models into financial services.

Retail banks have opportunities to break their data silos, combining traditional and new data sources in data lakes


Widespread adoption of analytics on top of massive data integration architecture has significant potential to create economic impact within the retail banking industry. Among the main mechanisms will be improved cross-selling and the development of personalized products. Analytics could have an especially large impact in reducing risk via better credit underwriting, credit monitoring, and improved collections. Retail banking is still undergoing digitizing many operation, and as this trend combines with a deeper use of data and analytics, new efficiencies can be realized in areas such as automating business support, targeting marketing more effectively, and improving customer engagement. The process of digitization itself will enable banks to generate more customer data while simultaneously taking advantage of lower-cost digital channels. Together these shifts could generate impact of some $400 billion to $600  billion annually, with roughly two-thirds of the impact realized in developed economies and the remainder in developing economies.

As the retail banking industry becomes more data-driven, it is likely to take on an entirely new look. Talent capable of crunching the numbers, developing the algorithms, and combining those skills with an understanding of financial services and the regulatory landscape will be critical. As more banking services become digital, physical branches could be phased out, leading to substantial cost savings.

Three main types of ecosystem players could emerge.

dollarFirst are the solution and analytics innovators. This category includes many of the fintech players. These digital natives have deep capabilities, and they tend to focus on a particular market niche, with no intention of expanding into full retail banking offerings. This frees them from the constraints of being fully regulated banks. Betterment, for example, offers wealth management by robo-advisers, while ZestFinance combines vast amounts of data and machine learning to improve credit scoring.

bankingSecond are incumbent institutions that are the natural owners of significant amounts of data describing the financial position and behavior of their customers. This proprietary data gives them a significant advantage, but they could lose it if they lose the source of the data (that is, their customers) or if other players come up with ways to create equivalent or superior information by integrating from different sources.

alipayThird, companies in other sectors can become part of the banking ecosystem if they bring in orthogonal data—such as non-financial data that provides a more comprehensive and granular view of the customer. These players may have large customer bases and advanced analytics capabilities created for their core businesses, and they can use these advantages to make rapid moves across sector boundaries, adding financial services to their business lines. Alibaba’s creation of Alipay and Apple’s unveiling of Apple Pay are prime examples of this trend.

The move toward massive data integration and analytics may create room for new players and partnerships. Acquisitions and collaborations may be necessary for traditional retail banks to acquire the talent and capabilities they need to stay relevant. Santander UK, for example, has launched a partnership with Kabbage, a small business loan provider that uses machine learning to automate credit decisions. Data-sharing partnerships between banks and telecom companies have helped both sides develop new insights (especially around fraud, customer creditworthiness, and microsegments of customers) that otherwise would not have been possible.Analytics-driven business models are emerging, such as Mint, an aggregator that utilizes data to create personal finance recommendations. Another new model involves peer-to-peer platforms. SoFi, for example, uses robust risk scoring through creative data sources and analytics to connect borrowers and investors directly; the company reports that it already has more than $6 billion in funded loans.


Moneta Capital First Closing English Version

Your Company Collect Tons of Data: Now What? Data can bring huge valve to business, but simply

The driving forces


The X, Y and Z generations were born into a world of mobile internet and apps. They are used to consuming content and services via smartphones and tablets. For these young people, most of today’s financial services are almost inaccessible. They don’t want to meet at the Bank’s branch and they don’t want an insurance agent. More importantly, when they need to make a decision, all of the pieces of knowledge are at their fingertips. They are clearly in need of a new breed of banks that feel more like Paypal and not like their parent’s bank.

Unbanked community

The majority of adults on this planet still do not have their own bank account. The global figure, including Africa and Asia, is over 2.5 billion. This huge segment of the population is in need of financial services and again, it seems unrealistic to believe that today’s banks could possibly satisfy that need. New mobile services, such as peer-to-peer payments, lending (and soon, also, insurance) will become more and more popular. A new breed of international wire services will replace the legacy of high-cost systems.

A technology revolution

The technology “tectonic” movements of the past three decades have resulted in a few powerful vectors joining forces to change the financial world: the Internet, mobile technology and cloud computing. Mobile technology and the Internet are perhaps much easier to appreciate, because we consume them in everyday life; however, it is really the high power of cloud computing that has laid the foundations for big data analytics. The Internet, mobile technology and high power computing on public or private clouds are the enabling technologies behind the Fintech and BFSI revolution. The data revolution caused Tim Cook, CEO of Apple, to declare at the Google Analytics User Conference: “If your user doesn’t pay, then he is not the customer, he is actually the product”.

The areas of disruption

Mobile payment

The smartphone and the tablet have de facto become the new PC – and more! It is hard to imagine any personal, or productivity task that is carried out today without a mobile device. All kinds of payment services, including bank transfers, or peer-to-peer payments, have transitioned to mobile.

Peer to peer lending

Not so long ago, only banks could lend money to people and businesses. Their relative advantage was the information that led to understanding risk. In the past few years, we have been witnessing new ventures that offer lending based on new breakthrough models. These models are based on a new kind of data, such as: social footprint, employment pattern, education, etc. The ramp up of these new lending services is phenomenal.

Robotic investment Trading

In the past few years, another area of innovation has emerged that is poised to reshape a major sector of the Fintech scene – Investments & Trade. Robotic trading platforms (low frequency) are the new big thing. In the USA only, tens of billions of dollars are transacted by robotic platforms that either provide advice (the Robo adviser) or run the entire show, including selling and buying stock automatically, based on a given set of rules.

Int’l wires

One area that has begun to witness major change is the international money market. It used to seem as though nothing had changed since the SWIFT systems were installed 40 – 50 years ago, to interconnect banks. And yet, these institutions are still moving trillions with no automatic routing, no on-line reporting, no free market and no choice of international wire providers. We are seeing more and more currency exchange and international wire providers on the cloud and these are poised to become a source for innovation

The Insurance tech is next

The wave of disruption that has hit the banking industry over the past few years is gaining momentum and is now set to impact the insurance industry as well. Insurance companies may benefit from multiple sources of innovation such as: new sensors (mobile technology) that will monitor the client’s lifestyle and provide on-line reports (at least in cases where the clients are offered a decent discount for allowing on-line monitoring). New big data analytics may revolutionize the way that risk is calculated and result in new models for policy pricing and new products. The number of startups pertaining to the insurance technology has been growing exponentially since 2015 and it is only a matter of time before this becomes one of the leading trends in Fintech.
CBInsights, March 2016: “Hundreds of new startups that claim to disrupt the insurance industry have been created and funded since 2015. This is clearly bigger than Fintech for Banks”. TechCruch, January 2016: “A change is coming to the insurance industry. And the opportunity is too big to be ignored for long. Beginning in 2016, we’ll start seeing established companies and newcomers alike move to fill insurance coverage gaps being created by new technology and industries”.

Moneta Capital First Closing English Version

Your Company Collect Tons of Data: Now What? Data can bring huge valve to business, but simply