We envision banks as these massive vaults filled with money. While there’s some truth to that assumption, your average bank is so much more. We can expect that banking is the next personal assistant.

Banks are massively complicated institutions that operate on a lot of different (often proprietary) systems working seamlessly in conjunction with one another. Anyone working in IT for a bank will tell you every day is a challenge.

Daily, banks manage to overcome a litany of internal and external obstacles — all while providing service with a smile. Long before anyone used online data and social media accounts to track people, banks could collect that sort of insight by following the money. Banks have access to a treasure trove of information about our financial preferences, which puts them in the position to act as concierges in our lives.

The ubiquity of solutions such as AI, blockchain, and machine learning can only serve to contextualize the future of banking further. Instead of focusing on terms and conditions, it’s time for banks to leverage the wealth of information provided by these solutions to engage people and help improve customers’ financial well-being.

Disrupting the Old Guard

Most banking decision makers see the role technology and innovation can play in upending the status quo. PayPal’s mobile platform prompted several legacy providers to work on their own mobile offerings, and Bitcoin showed the value blockchain brings to the transaction process.

The banking industry’s dated processes and operating systems are so entrenched that implementing widespread innovation is a challenge.

In the past, IT was just one part of banking. Now that IT is such an integral part of the banking business model, legacy providers must rethink their customer engagement tactics and reimagine their internal infrastructure. That new vision should start with reframing the purpose that modern banks serve.

For years, customers leveraged banks solely for their core financial services. That mindset persists through banking apps — users only see financial service apps for what the services they provide. The user doesn’t consider the banking app as entities that improve their financial decision-making.

Banking’s future isn’t in the actual products banks provide but in how they’re serving those products to customers.

We don’t need to understand the fine print of a mortgage or insurance policy if we can understand the value it brings to our daily lives. For example, mortgages documents, insurance, CDs, stocks, 401(k)s, and numerous other products come with polished banking terminology and drawn-out contracts that can perplex even the best lawyer.

But imagine a banking platform that breaks legalese down into digestible bits. Instead of prompting you to click forward to complete the process, envision using an app that compiles all your past and current financial information and advises you regarding potential next steps.

Banking that can genuinely help us live our lives a better way — or any fintech company in this mold are going to be the next big thing.

Technology can be the catalyst to kick off this shift, but banking decision makers must reconstruct the roles they play if they want to bring this tech-reality to life. Instead of cold, numbers-driven institutions, consumers wish to have banks that offer friendly, knowledgable and familiar faces. A familiar and friendly help is where banks can start to become our personal assistants.

The Next Frontier of Banking

People like to say money isn’t essential, but it’s the fuel that runs our lives. That makes our banks the engine, and financial institutions that genuinely figure out how to steer us toward better lives will become the Ubers and Teslas of the financial industry. Thankfully, there are already financial companies pushing to provide better personal assistance.

Manulife, for example, can tie Fitbit and other fitness tracking data into its Vitality program. Life insurance companies have a vested interest in keeping people healthy, and Manulife uses discounted premiums and other loyalty perks to encourage its members to lead healthier lives.

Back in the banking arena, TD Bank’s “TD for Me” feature uses mobile data to send customers real-time, contextual offers while they’re on the go. Using a feature while on the go gives consumers peace of mind to make financial decisions no matter where they are.

The Royal Bank of Canada is taking things a step further, integrating an Airbnb-like product into its offerings. The Canadian bank is constantly innovating its tech offerings to ensure the brand stays relevant, creating person-to-person marketplaces and ecosystems that align with current consumer expectations.

Consumers still want banks for the same basic needs they’ve had for generations — to finance houses, cars, businesses, vacations, etc. The way we think about those basic needs has evolved, though. The key moving forward is for banks to understand what truly motivates people to spend money.

Right now, we have more data available to accomplish that task than ever before. Companies that figure out how to leverage data and bridge the trust gap between consumers and banks will become the next big unicorn companies.

The post Banking is the Next Personal Assistant appeared first on ReadWrite.

Estonian Prime Minister Jüri Ratas arrived at the World Economic Forum’s annual meeting in Davos, Switzerland, in late January with praise for his country’s digital identity program. Estonia has one of the world’s foremost digital identity programs, which allows its citizens to use their identities to vote, buy medication, sign documents, and more.

Our digital identity isn’t just about Social Security numbers and names — it’s all about context. Our behavior at home differs from when we’re traveling or at work, and our digital identities should operate in accordance with those distinct behaviors.

This kind of context will dictate the future of identity. It isn’t just about attributes populating a database — it’s about building an evolving information repository that safely supports a person’s lifestyle and decision-making.

The Evolution of Personal Data

We’re used to looking at identity through the lens of the system of record, one where everything about you can be summed up in neat, tidy spreadsheets. But we’re simply not comfortable being so open with such delicate pieces of information, especially in the aftermath of significant hacks to systems like Equifax, Sony, and Yahoo.

What many people are comfortable sharing is their background information, their employment histories, and even their hobbies.

This type of personal information can be just as useful in identifying us without making us feel like we’re exposed to the outside world.

It’s the type of information that marketers use when creating ads and placing ads on Facebook; it’s even the information we mine when meeting or dating a new person. Why not lean into this version of identity?

As an end user myself, I’m willing to share special information in an automated fashion so long as I’m not sharing sensitive information with you.

This type of sharing gives the company access to valuable sensory and contextual information and introduces me to better products and services.

Though that might sound a bit abstract, I think we can look at it as an evolution of identity. Not only that, but it’s a type of personality that’s more conducive to innovation, especially in an increasingly automated digital world. For instance, dating apps like Bumble allow users to connect their Spotify accounts to their profiles; a feature helps users form more immediate connections without having to provide more sensitive information.

This isn’t to say that there’s no friction in this new digital identity. Despite the potential for better personalization, some customers still don’t want to share any of their data.

Innovative entrepreneurs and researchers should see this as an opportunity to empower the next generation of personalization tools and services. Blockchain startups, for example, are quickly realizing that this trusted distributed ledger is preferred by many over single companies like Facebook or Google holding all the cards.

What’s important to note here is that blockchain isn’t incremental in innovation — it’s as disruptive as Tesla was to the automobile industry. Contextual data is a radical innovation with a new engine that doesn’t require burning fossil fuels. It’s a game-changer, and those that leverage this digital identifier will evolve along with it.

A Stronger (Digital) Sense of Self

The information in a digital identity shouldn’t just feel unique to a user; it should feel safe from unwanted intruders.

To make digital identities a more secure (and viable) option, enact these three strategies:

1. Create a transaction framework. We created an excellent communication framework in the form of the internet. It lets us view video from another location, FaceTime with family and friends, and even move data anywhere in the world. A transaction framework is needed on top of this layer to connect the internet with emerging solutions such as AI, IoT, and edge computing.

Blockchain’s distributed and decentralized ledger is the latest innovation in this space. Startups are already leveraging this technology to create and protect transactional digital identities. More importantly, the public is starting to accept this technology. Bitcoin Market Journal estimated that 22 million customers used the cryptocurrency in 2018. A transaction framework puts the necessary infrastructure and safeguards in place to foster a secure storage space for digital identities.

2. Leverage encryption and machine learning. With legacy tech giants like Google and Facebook losing trust, the internet is already evolving, and one way is through the use of machine learning and personalization on encrypted data.

From a storage point of view, it’s no longer necessary to depend on large data warehouses to be centralized data repositories. Data can be split and encrypted to ensure that each person connected acts as a node without ever having access to the files they store.

Pretend my personal data is a pencil. I can wrap tissue paper around the pen and hand it to you, and you’ll know it’s a pencil. You may not know the exact specifications of the pencil, but you understand through context what I’m giving you. This environment is ripe for AI disruption. Machine learning will dominate this space within the next decade as algorithms learn to identify you by your transactions.

3. Implement privacy by design. Government regulations around the world already are working to embed privacy into every new design. Regulations like the European Union’s General Data Protection Regulation and the second Payment Services Directive are creating a privacy-conscious ecosystem that makes many businesses scoff, but I think it’s a positive note.

The definition of identity is changing in the way that we no longer need to reveal sensitive information to receive personalized offers and services. Thanks to companies like Deloitte, anyone can implement Privacy by Design into their business to ensure the implementation of 30 pieces of measurable privacy criteria and 107 privacy controls. If you can pass the Privacy by Design criteria, you’re prepared for the coming changes.

Human identities evolve constantly, and with the emergence of digital identities, the way we view data — and ourselves — is also changing. We’re at the dawn of a new, trust-based world. How are you preparing?

The post As Digital Identities Evolve, Data Security Should Too appeared first on ReadWrite.

Securing venture capital funding is a tricky terrain to travel. It’s hard enough for founders to accrue the capital needed to continue scaling, but it’s even more difficult for entrepreneurs from underrepresented demographics.

In a recent study jointly published by Babson and Wellesley colleges, it was found that just 3 percent, or $1.5 billion, of the $50.8 billion in VC funding handed out between 2011 and ‘13 was raised by women. And companies with all-male executive boards were four times likelier to garner funding than boards that included at least one woman.

Optics such as gender and race can at times dissuade VCs from supplying worthy companies with the funding they need. But what if VCs awarded funds by using a blind approach to assessing a company’s potential trajectory? Artificial intelligence informed by concrete data could lower that curtain, crafting a future in which machine learning helps VC funding lean less on appearances and more on a company’s potential merit.

Scant funding for minority and women-led startups is an issue that’s been building for some time. Less than 1 percent of VC funds raised go to minority-run business, while 2 percent goes to companies fronted by women, despite the fact that 38 percent of U.S. companies have women in charge.

Trends like that, no doubt, prompted Dell entrepreneur-in-residence Elizabeth Gore to create Alice, an AI platform that uses a litany of data points in order to open female, minority, and LGBT founders up to greater VC funding opportunities. Biases also exist in favor of younger entrepreneurs or those from certain universities. By using AI, investors can leave behind biases that they may not even be aware of and focus solely on a company’s merits as an opportunity for returns.

In the PricewaterhouseCoopers Digital IQ Survey of 2017, 52 percent of professionals in the industry reported making “substantial investments” in AI, and two-thirds expect to be doing the same three years from now. Perhaps even more telling is that 72 percent of business leaders and decision makers picked AI as the most compelling future business advantage.

The metrics and data points that define successful startups are becoming increasingly visible and increasingly repeatable, giving investors a recently accessible degree. AI lets entrepreneurs align their metrics with a successful blueprint. For VC firms, it’s a chance to focus less on closing deals and more partnering more diverse, high-quality startups.

Venture capital is an industry that revolves around people and relationships, but it doesn’t come without its own risks. VCs may relate better to individuals who resemble themselves at different parts of their career, and in a male-dominated business, this might be one reason for the existence of a systematic bias toward men.

June Manley saw that bias firsthand when she pitched her software enterprise company in 2015. She participated in more than 80 VC meetings, repeatedly witnessing funders disregard her product, condescend to her about her qualifications, or even suggest her husband take the lead when pitching to VCs. She even witnessed similar companies fronted by men get the nod as she went to meeting after meeting looking for someone to take a chance on her.

From that frustration sprung Female Founders Faster Forward, a nonprofit organization that uses a tech-based model designed to minimize that type of bias. Using a Startup Investment Model Index, a kind of startup FICO score based on attributes from some 750 VC-funded businesses, the software will be an evolving entity that female founders can use as a complementary resource to shield their funding quest from bias.

This fluid, AI-inspired approach will use metrics such as startup risk and maturity to compile a score that founders can attach to their startups and use in the funding process. Manley hopes the tech will help raise female funding from 3 percent to 20 percent by 2020.

Data and figures can cut through whatever potential biases a VC might have when it comes to funding companies. Machine learning can sift through metrics and stray from any biases a VC might have and drill down to the numbers that will ultimately point to a startup’s chances for success.

AI can establish a different kind of relationship, one that hinges more on what the data says about a company’s potential and less on any personal connection or potential biases. For any AI product or startup to be successful, there needs to be data. Feeding empirical information into an AI engine allows engineers to confirm their theories and demonstrate its impact. Without data, there is nothing to learn from, no matter how effective the algorithm.

AI never stops learning, which is why it’s an ideal match for VC firms. Data and numbers are unencumbered by personal bias, free to assess bodies on the data in front of them. As that information continues to pour in and change from minute to minute, a VC can take startups at face value, making decisions on the potential a company brings to the table instead of who is sitting across it.

The post AI Is Key to Bias-Free VC appeared first on ReadWrite.

The current notion of personalization is fairly shallow. Users can customize the kind of content they receive via Google Alerts and blog or website subscriptions, but not in the way an underlying infrastructure handles it. For comparison, this is like being able to choose your car’s color but not the make or model.

For personalization to carry any meaningful benefit, it must begin to impact the algorithms and infrastructure at the lower stacks of digital interactions. Privacy is paramount for users, evidenced by the University of Phoenix findings that nearly two-thirds of U.S. adults on social media claim to have been hacked.

Users have constantly evolving levels of control when it comes to how pictures, videos, audio files, and other pieces of online content are handled. Now, the opportunity exists to follow the same scheme in terms of algorithms. Open algorithms give all users, regardless of technical ability, the level of control and transparency that’ll help them feel sure that their personal information is secure.

The Opportunity of Open Algorithms

A group that includes MIT Media Lab, Orange, and the World Economic Forum is pioneering this movement through its Open Algorithms project, known as OPAL. Essentially, the goal is to create a hub of anonymized data to which everyone from telecom providers to major banks would contribute. Then, the users themselves would take advantage of visual tools to connect relevant data sets in ways that satisfy their individual needs and wants.

The brief history of the internet is filled with swift failures on a massive scale. Facebook’s Cambridge Analytica debacle is proof that even major players would be wise to use new business models to secure long-term viability. The concept of open algorithms creates new challenges, but it also provides sweeping opportunities for monetization as end users become co-developers on a platform.

The current model of hoarding user data to predict what kind of advertising they want to see is primitive and outdated. In an ecosystem of open algorithms, Facebook and Google could inspire users themselves to choose the advertisements they want to see, leading directly to more conversions. Businesses can utilize this data to craft ads that best approach a target audience: Instead of guessing what an ad should contain, data tells you what might pique a target audience’s interest, leading to more targeted messages and a potential for more conversions. Ultimately, this approach benefits users, developers, and advertisers in ways that are too exciting to ignore.

Once the concept of open algorithms reaches maturity and widespread adoption, it will lead to sweeping changes in how we understand digital interactions:

Services Will Become Decentralized and Democratic

A company like Google or Facebook will still be the core enabler of a service, but the end user will have far more control over the behavior of the platform. Certain protocols will still exist, but users will be able to set up groups and behaviors that are flexible and variable. They will be able to control their interactions to a much greater extent while contributing to the platform in meaningful ways.

Google and Facebook’s new location-sharing offerings — Location Sharing and Live Location, respectively — could present privacy issues but represent a step forward in the open algorithm discussion. In Facebook’s case, the live location-sharing feature is embedded in the Messenger app and allows the user to share his or her real-time whereabouts for one hour.

Each service allows users to control how much information is shared about their locations and, more importantly, who can track them. If these users continue using location platforms and other offerings that utilize open algorithms, they’ll want to know how their information will be protected within those ecosystems.

Users Will Own Information and Form Independent Connections

Services like Amazon or Spotify exist inside rigid silos. Currently, Facebook monopolizes 50 minutes daily of the average user’s time. In a future of open algorithms, users will be able to link and travel between platforms with far greater freedom. We will no longer think of platforms as independent islands, but rather as building blocks we can construct according to our own agendas.

You’ve seen Facebook, Twitter, and other social media platforms link to an Instagram. But now you’re seeing Spotify partner with Facebook to import contacts, show what friends are listening to, and let users share music on their timelines and discover new artists.

The open algorithm movement could eventually lead to Facebook users being able to customize their own Spotify dashboards on the site and upload playlists and favorite songs from there. Sites that are one-stop shops for measuring social media imprint are nothing new, though very few give users the chance to balance all their social presences in one spot.

Orange, meanwhile, is teaming with colleges and universities to help cultivate public-private relationships surrounding big data. Individual schools can compile information and use that data for respective bits of social good. This is just a small glimpse of what we could see as open algorithms become more prevalent.

Closed Algorithms Will Fight for Survival

The internet is locked in a battle over more control versus more democracy, one that will soon begin to affect core players who are currently committed to closed algorithms, but it won’t be easy to convince companies with traditional “I own your data, so I’ll sell your data” business models, which depend entirely on “black box” data and privacy approaches, to open their ecosystems.

Amazon’s algorithm for compiling “frequently bought together” recommendations has been criticized for something along these lines. Just five days after a subway explosion in London last year, British shoppers received suggestions for bomb ingredients while perusing the site for groceries and other accessories. Earlier that year, a U.K. newspaper noted, Amazon had bomb-making books on offer just days after a terrorist attack in Manchester. After the London attack, members of Parliament asked Amazon to look into tightening up its algorithms.

While mistakes like Amazon’s are avoidable and unfortunate, the financial potential for open algorithms will be too much for closed-algorithm enthusiasts to combat. In fact, an Accenture study found that 75 percent of consumers will buy products that address them personally or remember past purchases.

The convenience factor of open algorithms puts users in the driver’s seat to control everything they see and don’t see. Taking out that middleman could allow businesses to charge more on services, meaning customers may gladly pay more for personalization options.

New Business Models Will Develop and Dominate

Similar to the way peer-to-peer file sharing disrupted the music industry, open algorithms will disrupt the information economy. Facebook is a dominant marketing platform, one that, according to Social Media Examiner, is used by 93 percent of social marketers. But the future success of Facebook will not be owed to the amount of data it has control over. It’ll come via the amount of agency that users have over their own data. The flexibility of open algorithms is what will continue to drive traffic to the platform and continue to make it an appealing ecosystem for advertisers.

Open algorithms are a technical challenge, but the greatest hurdle to adoption is one of attitudes and cultures. Users are already clamoring for the kind of freedom that open algorithms provide. The platforms that embrace, rather than fight, this freedom will be well-positioned for the next generation of online life.

The post Algorithmic Transparency Is the Next Disruption for Tech Companies appeared first on ReadWrite.