Offline Versus Real-Time Analytics: Where Is the Industry Heading?


analytics-11-22-17.pngFinancial institutions are demanding real-time analytics at their point of customer interactions. Why? Sophisticated analytics applied in real time and at the point of customer contact can deliver better customer experience as well as increase the financial results of the institution. For example:

  • An insurance company can match different combinations of coverages and add-ons that can fit within a customer’s given constraints on price.
  • A banker receiving a phone call can see on screen the updated Life Time Value (LTV) of the customer and hold the discussion accordingly.

For years, we have been advising our clients to connect their front-end, customer- facing systems with real-time pricing analytical capabilities, or at least lay the foundations to enable this capability in the near future.

According to a September 2016 report from the research firm Gartner, “Between 2016 and 2019, spending on real-time analytics will grow three times faster than spending on non-real-time analytics.” Getting the right real-time analytics at the right time can deliver great value. Yet, from my company’s standpoint, most of the questions we get about real-time pricing engines are from vendors of front-end systems and other stakeholders. They are approaching us to enable the integration of their systems with their client’s back-end pricing structures. These are providers of insurance rating engines and underwriting solutions, as well as providers of core systems, revenue management and onboarding systems.

It seems that the driver for this vendor interest is explicit demand from the banks and insurance companies themselves. These institutions are increasingly investing in off-line pricing analytics to improve performance, software that can be used to optimize pricing and decision making.

Why Is This Happening Now?
The rush to utilize real-time analytics in customer-facing processes and decisions is not unique to pricing nor to the financial services industries. It has been growing for several years as part of the broader big data and advanced analytics trends.

Banks and insurers are now raising real-time pricing analytics as a requirement from suppliers of pricing systems, and have been defining such capabilities, or connectivity to such systems, as must have “add-ons” in requests for proposals for core and front-end systems. For example, banks and insurers are demanding real-time analytics for systems that offer customer relationship management, underwriting, onboarding, rating and pricing. Of course, the level of demand for such pre-integration differs between countries and sub-industries, and it is highly influenced by regulatory requirements, however, in most segments we have noticed the pull in this direction.

Moving From Off-Line Analytics to Real-Time Analytics
Today, it is even easier for financial organizations to get their budgets to include expenses of adopting real-time analytics. Replacement of core systems is accelerating as more resources are available to buy and implement these systems. This is enabling companies to re-evaluate all related processes, including pricing. Coupled with the surge in analytical know-how and advances in analytics technologies, including real-time capabilities and faster optimization, real-time analytics is becoming more widely feasible.

But the underlying benefits of real-time analytics is what is really driving the demand. Financial institutions realize that connecting their offline analytics to the customer facing process brings uplift not only in numbers but in the customer experience itself. According to a December 2016 report from the research firm Gartner, real-time analytics at firms is facilitating faster, more accurate decisions, especially for complex digital business initiatives such as online and mobile banking. Below are some of the benefits we have seen customers enjoying after migrating to real-time analytics:

  1. The ability to react quickly to aggressive competition, especially given the rise of direct channels and players.
  2. Improvement in the efficiency of price execution processes as well as a reduction in time-to-market of new pricing strategies.
  3. Improvement in customer-facing decisions. Once a company has a system in place to analyze real-time data, their ability to understand the customer significantly increases, translating into improvement in key performance indicators such as annual increases in pricing, as well as being able to anticipate and meet customer expectations.

Is Real-Time Analytics on Your Roadmap for 2018 or Beyond?
Regardless of what the reasons might be, we have been receiving more and stronger indications that real-time analytics is catching on in the insurance and banking markets in which we operate. Offline advanced analytics are already mainstream investments in financial organizations, and the focus seems to be progressing very practically to the next logical extension of real-time application of these analytics. Implementing real- time analytics that is connected to customer-facing systems requires forethought and planning. Even if this is something you are considering doing three years from now, the planning should start today.

To discuss how these topics impact your business, feel free to contact us at info@earnix.com.

How Investments in Data Quality Boost Risk Management Productivity


data-10-4-17.pngA direct correlation exists between data quality and productivity improvements within the risk management function. Poor quality data can result in increased time to develop models, lower confidence in the model results and less time to analyze results. Less precise modelling caused by poor data quality can mean that banks have to set aside higher capital buffers and loss allowance provisions.

There are numerous processes available for banks to define data quality, and guiding principles that can be implemented to improve data quality. When defining the firm’s framework for data quality in risk analytics, the following guidelines can be applied:

  • Specifically document how data are defined and constructed.
  • Ensure that data accurately quantifies the concept that modellers intend to measure.
  • Independently verify numerical correctness by using backlinks to primary sources, quality declarations, unique identifiers and accessible quality logs.

Moody’s Analytics recently published an article entitled “When Good Data Happen to Good People: Boosting Productivity with High-Quality Data.” This article quantifies the impact of data quality on improvements to analytical productivity, and provides a functional definition of data quality along with detailed examples of the impact of improving data quality on efficiency in analytical tasks. To read the full whitepaper, click here.

Myth-Busting: Data & Analytics is a Big Bank Game


data.png

Consumers today are increasingly reliant on having data at their fingertips to make decisions and, most importantly, to simplify their lives. Companies like Amazon and Starbucks have set the gold-star standard when it comes to using analytics to understand customer preferences, as well as simplifying the purchase experience. Now those expectations are carrying into every aspect of our life, and several industries, including banking, find themselves playing catch up.

Banks of all sizes are struggling to meet these expectations, but mid-tier Banks (defined as $10-$50 billion in total assets) in particular find themselves at an inflection point with regard to data and analytics. Data and analytics are central not only to building a more loyal customer base, but also to creating greater efficiency to compete more effectively. However, mid-tier banks have the advantage of being more nimble relative to their larger competitors, allowing them to create better customer experiences and greater efficiency—even with smaller technology budgets.

For any financial institution, there are four levels of data and analytics maturity:

Limited. Many of these banks still rely heavily on intuition to make decisions. This is due primarily to lack of leadership involvement or support; lack of technology spend on architecture, talent or tools; and overreliance on ineffective legacy systems. These institutions need to get beyond those daily challenges to realize strategic benefits that will grow revenue, cut costs, mitigate risk and improve customer experiences.

Recognized importance. This level is likely the most difficult achievement, and will take more time than other transitions. These institutions have successfully garnered executive support, established a shared vision across the organization, and analytics use cases are taking shape with some small victories. But don’t bite off more than you can chew. The most successful banks start with small, focused use cases and build on what they have learned.

More advanced. For most institutions, achieving this level would be sufficient for the long-term. It takes approximately six to eight years to get here and at this level, the volume, variety and velocity of data begin to “explode” within the organization. New roles are required to manage this process, there is typically a centralized data warehouse in place, and analytics are a core part of strategic planning and budget processes. And notably, there is a strong understanding and receptiveness for data and analytics from the front lines all the way to the board of directors.

Culturally ingrained. While everyone will strive to achieve this, few will get to this level—and that’s okay. The benchmarks are set by a cross section of both banks, and non-bank powerhouses like Amazon and Nordstrom. At this level, the institution is well known for their analytics prowess. Predictive analytics are firmly in place, they are looking at using unstructured data, exploring more advanced analytics techniques (i.e. AI, IoT, blockchain), and are heavily focused on insight generation. Impressively, much of their analytics are real-time and alerting is in place to help decision makers better interact and please customers and prospects.

So how do you successfully progress forward on your data and analytics maturity journey? By focusing on the five data and analytics essentials:

  • Strategic Support and Adoption. For analytics to progress, it needs to be part of the fabric of the institution’s vision, strategic planning and day-to-day activity for decision makers.
  • Information Architecture and Governance. The most significant decisions are made here, but it is also where most institutions make mistakes. It requires a long-term view, significant investment over time, and both management and technical talent to execute properly.
  • Data and Analytics Capabilities. This area encompasses both the volume and types of data sources, the scope of the data requirements, the integration necessary to properly turn the data into analysis, and the toolsets used to organize, report and deliver the analysis.
  • Data needs to be both timely and available to succeed. Naturally, both the timeliness and accessibility should increase as the organization progresses on its analytics journey.
  • Organization and Cooperation. While often a roadblock, collaboration and cooperation for data and analytics across the organization is critical to success.

To be sure, the data and analytics journey is a long-term process. Levels can’t be skipped and progress must be “learned and earned.” So is this worth it? Is the ROI for these capabilities going to be meaningful? The answer is a resounding “yes.” Mid-tier banks that have achieved even the “recognized importance” level are seeing as much as a 20 percent improvement to efficiency ratios and 15 percent improvement in return on assets, respectively, as compared to less mature banks over the last three-year period.

Rob Rubin, director at Novantas, is the co-author of this piece.

How Advances in Machine Intelligence Will Benefit Banks


artificial-intelligence.png

Over the past two years, banks have enthusiastically embraced robotic process automation (RPA), and for good reason. Offloading mundane work to bots (apps that perform automated tasks) can help banks improve efficiency and allow employees to focus on high-value work requiring human judgement and skills, all of which can help increase productivity, profitability, and customer and employee satisfaction.

At the same time, scientists have been making tremendous progress in big data analytics and artificial intelligence (AI). It’s been an era of amazing innovation and research acceleration, with many technologies maturing in parallel. The stage is now set for the third strand of automation in banking: AI (also known as cognitive computing) and machine intelligence.

AI, Machine Intelligence and Your Bank
Broadly speaking, AI and machine intelligence are computer systems that mimic human intelligence. AI is used for performing human tasks, whereas machine intelligence is an umbrella term for a broader collection of cognitive tools that have evolved significantly in recent years: machine learning, deep learning, advanced cognitive analytics, robotics process automation and bots, to name a few. They have been around (and evolving) for decades but innovations and new capabilities are enabling banks to apply them to a rapidly expanding set of business problems.

For example, banks are improving customer service by using AI to learn from customer behavior and deliver more precisely on customer preferences, tailor the customer journey and streamline product and credit acquisition. Using AI on repetitive tasks is helping banks find new ways to increase productivity and reduce costs—carefully selecting the next best action or responding efficiently to customers via cognitive call centers, for example. And AI is helping banks lower the risk of human error by reducing human involvement in cyber, credit, fraud, compliance, internal audit and employee retention.

Bots and RPA have demonstrated their value and reliability on straightforward tasks, building confidence and interest in more sophisticated uses of AI machine intelligence such as:

  • Robots that answer complex financial questions posed in plain English.
  • Cloud-based software that can potentially answer more the 65 million questions by scanning drug approvals, economic reports, monetary policy changes and political events and their impact on nearly every financial asset on the planet.
  • AI to help organize customer data and create customized packages of personalized advice, delivered to bank customers via their mobile phone.
  • Linguistic analysis and trading compliance technology to help monitor and prevent trade malpractice.

We’re far from peak adoption, but banks are quickly moving past pilot stage as they discover powerful ways AI and machine intelligence can improve multiple areas of business.

Machine Intelligence is Not Just the Ability to Do, it’s the Ability to Learn
In addition to making some things easier, faster, more accurate and more reliable, AI and machine intelligence enable you to do things that simply weren’t possible before.

For example, say your bank needs to review every contract to confirm compliance with new regulations. An AI system can search all contracts and insert specific language if it’s missing, saving considerable time and resources.

AI also enables the use of intelligent systems where, for example, employees can check in with HR on payroll, vacation time, professional development credits and so on. The system translates queries from written text to actual meaning and returns results in plain narrative. If it can’t answer a question, an HR specialist can pick up the conversation from the transcript while the AI watches, listens and learns. Gradually, the AI becomes as effective as the trained HR professional, who can now focus on other, more complicated tasks.

And computers can analyze data in an unlimited number of dimensions—a much more powerful view than the human limitation of three or four dimensions. If your bank is trying to understand which clients are more likely to engage in fraud, you can use AI software to look for suspicious behavior, a far superior alternative to reliance on people and older types of technology. This is of incredible value: Banks are obligated to pursue alerts but the vast majority are irrelevant. AI can eliminate false positives, reducing the amount of unnecessary activity significantly.

AI is Ready for Banking, but is Your Bank Ready for AI?
Right now, AI bolts onto a robotics environment, providing capability that enables robots to interact with humans or computer systems with more intelligence. Building these systems requires people with experience in the different technologies, but they have become relatively easy to implement: Nothing is out of the box—yet—but the enabling components are in the marketplace. The systems are cost effective, in large part because of the basic benefits they provide—fewer errors, faster throughput and greater productivity (24×7, no vacations).

Bottom line: If your bank is ready for AI, AI is ready for your bank.

Contributed by: Sridhar Rajan, Principal, Deloitte Consulting LLP andDave Kuder, Senior Manager, Deloitte Consulting LLP

SizeUp: Friend or Foe


size-up.png

Making smart decisions at every stage of growth is a critical—and often difficult—process for many small businesses. While larger companies have the money and resources to utilize big data and analytics tools to gain insight into their performance, customers and competition, small businesses are often left guessing and must rely on incomplete information (or gut instinct) to make key decisions like whether to expand into a new location or introduce new products.

That’s where fintech business intelligence startup SizeUp is stepping in. SizeUp partners with traditional banks to offer big data and business intelligence tools to small business customers to engage (and retain) them over the long run. Business owners who want to know such things as the most under-served areas of their markets when they are considering where to expand can use SizeUp to make the best possible decisions.

SizeUp already partners with big banks like Wells Fargo & Co., but long term will it be a friend or foe to legacy institutions? Let’s dive in and find out.

THE GOOD
SizeUp was initially chosen as one of 30 finalists in the TechCrunch Disrupt startup pitch competition in 2016, out of more than 12,000 applicants. TechCrunch Disrupt is Silicon Valley’s leading startup and technology conference, and the Disrupt startup pitch contest is widely considered to be the most competitive in the tech world. One of the important benefits that banks derive from working with SizeUp is that it increases the breadth of services they can offer their small business clients. Wells Fargo’s Competitive Intelligence Tool (powered by SizeUp), for example, helps businesses manage and grow their companies by analyzing performance against competitors, mapping out customer opportunities and finding the best places to advertise in the future. Providing this level of intelligence about local markets, along with a competitive scorecard analysis, can also be used to decide the best areas for potential expansion.

And as successful small businesses scale, SizeUp’s platform is designed to enable banks to anticipate which financial products their clients are likely to need in the next stage of growth.

“SizeUp enables banks to introduce their products and services at each key decision making moment in a business’ life,” says SizeUp CEO Anatalio Ubalde. “So for example, a small business loan during launch, and a line of credit as they grow.”

Big banks quickly realized the value that SizeUp’s platform brings to the table, with institutions like Deutsche Bank and Credit Suisse investing early on in SizeUp’s development through programs like the Plug and Play Fintech Accelerator. Headquartered in France, Plug and Play is a large international fintech venture capital firm and accelerator, and a partner with BNP Paribas, France’s second largest bank. SizeUp has even partnered with the U.S. Small Business Administration to help entrepreneurs and business owners assess how they stack up with the competition and map out potential vendors and suppliers.

THE BAD
It’s hard to find a whole lot of negatives with SizeUp’s platform and partnership model. If there’s one drawback, it’s the sheer volume of data points and information that is available on the platform. SizeUp draws from hundreds of public and private data sources, so the platform might be slightly overwhelming for small business owners who are not particularly tech savvy. That being said, banks are in a good position to aid their small business clients onboard to the platform and accelerate the learning curve.

OUR VERDICT: FRIEND
At the end of the day, SizeUp is a friend to banks and legacy financial institutions of all sizes. Bringing this level of sophisticated big data and business intelligence to their small business clients is only serving to help them grow and succeed, which should ultimately result in increased small business account retention. And as these companies grow, banks can be ready to upsell and cross-sell additional products and services that focus on specific stages of development along the way. SizeUp also provides an engaging product and interface that business owners can use for a variety of purposes, from plotting out an advertising campaign to gaining an in-depth understanding of how they stack up against the competition at any given time.

Big data and sophisticated business intelligence is something that most small business thought was only for companies with large technology budgets, but SizeUp is in the process of changing all that. And in addition to helping small businesses make better decisions during each phase of their growth, the firm is helping banks engage (and retain) those customers over the long haul.

How to Become a Data-Driven Bank


data-5-8-17.pngBanks collect lots of data on their customers, but they aren’t always adept at using it to grow their business. Community banks, in particular, are just beginning to realize the power of data analytics and business intelligence.

Client data and the tools to analyze it can transform how banks conduct their commercial lending business. Data-driven banks can leverage analytics to make better informed decisions, streamline operations, and improve customer service.

The following are three steps for boards to consider for successful adoption of better data analytics:

  1. Support investment in systems that organize and centralize data and standardize processes.
  2. Reinforce the systems investment with policy, training and change management initiatives.
  3. Champion the new systems and processes and how they contribute to the bank’s success.

Here are some practical recommendations for a community bank executive who wants to turn data analysis into bottom-line results.

Define the data universe. The data that community banks can use includes company financials, qualitative customer data, and borrower behavioral data, including payment and credit utilization history. Establishing a centralized system that captures this unstructured data consistently is the first step in this process.

Consider a partnership. Effective analytics strategies ensure that short- and long-term goals are aligned with the bank’s current business operations. Partnering with a vendor with the required analytics technology and implementation expertise could help the bank capture the right data and integrate it into their processes.

Data quality is key. The top tactical issues with this approach involve collecting, organizing, and protecting the quality of the data. Maintaining the integrity of analytics requires clean data that is accurate, comprehensive and continually updated. Data quality is key to realizing the value of business intelligence tools.

Communicate early and often. Educating the organization on the value of credit measures, whether back office risk managers or front office sales professionals, will equip all stakeholders with a solid understanding of the new analytic tools and how they support the overall goals of the bank.

Establish Success Metrics. Even data-driven banks should be wary of aligning internal data with external benchmarks and best practices, because the latter may not be applicable to a particular type of business, product focus, marketplace or strategy. Instead, banks can use internal data to define their own benchmarks and measure success against goals and past performance. Assessing actual performance by comparing historical trends to new profitability, default and recovery metrics (including internal ratings) serves as an indicator of improvement. In other words, how would the prior portfolio perform given new tools and measures versus its actual performance?

Leveraging advanced data analytics and business intelligence tools is an investment that, if properly implemented, should pay dividends in the form of higher quality loans, better customer service and increased operational efficiency.

To read the complete white paper, “How to Become a Data-Driven Bank,” click here.

A Bank CEO Manages the Risks of Doing Business with Fintechs



Not all banks are comfortable taking on the risks of partnerships with startup fintech companies. Mike Butler is the president and CEO of Radius Bank, a $1 billion asset, Boston-based bank with three offices, and a national customer base serviced through innovative online and mobile technology. He explains how he handles the risk of doing business with fintech companies.

The video includes information on:

  • Radius Bank’s Approach to Vendor Risk Management
  • Regulatory Concerns
  • The “Wall” That Protects Customer Data
This article first appeared in the Bank Director digital magazine.

How Can Your Bank Tap Into the Internet of Things?


internet-of-things-3-28-17.pngThe Internet of Things (IoT) has officially moved beyond hype. IoT is now well known and defined—basically putting data-gathering sensors on machines, products and people, and making the data available on the Internet—and companies are already using IoT to drive improvements in operational performance, customer experience and product pricing. Gartner predicts we’ll see 25 billion IoT data-gathering endpoints installed worldwide by 2020.

While IoT is delivering on its promise in a wide range of industries, many bankers are still struggling to find the value in finance, an industry largely built on intangibles. We see two primary IoT opportunities for banks:

  • Direct use of sensor data (location, activities, habits) to better engage customers and assess creditworthiness.
  • Partnering with companies that manufacture or integrate sensors into products to provide payment services for device-initiated transactions.

Engaging customers and assessing creditworthiness
Like most businesses, your bank can simply use IoT to understand—and serve—customers better. Banks are already implementing smart phone beacon technology that identifies customers as they walk in the door. Customers who opt in can be greeted by name, served more quickly and generally treated with more personalized care. You can also take advantage of sensor data outside of the bank to market more relevant services to customers. For example, data from sensors could […]

This content was originally written for FinXTech.com. For the complete article, please click here.

Smart Data Emphasizes Quality, Not Quantity


big-data.png

International Data Corp. (IDC) suggests that worldwide revenues for big data and business analytics will grow from $130 billion in 2016 to more than $203 billion in 2020. The commercial interest in data comes as no surprise given the immense role it plays in facilitating innovation in the financial services industry and beyond. After all, for banks of any size, data is at the core of their vital business decisions. It enables the appropriate risk assessment of every financial operation and allows banks to accurately estimate the creditworthiness of existing and potential customers, among other things.

The value of data, however, has long been correlated with its quantity rather than quality, laying a foundation for big data analytic tools and intensive data generation in relationships between companies and consumers. While we can’t deny the value of such an approach in displaying major industry trends and assessing customer groups on a general level, financial technology startups nowadays are proving that innovation in the financial services industry will likely come from a smart use of more limited, but higher quality data rather than its scale. In addition, given the diversity of sources and ever-accelerating speed of data generation, it becomes more difficult to drive meaningful insights.

Smart Data’s Value as Raw Material
Smart data represents a more sophisticated approach to data collection and analysis, focusing on meaningful pieces of information for more accurate decisions. Coupled with advanced capabilities of AI and machine learning, smart data presents an opportunity for startups to efficiently derive deeper insights from limited, but relevant data points. Professionals from Siemens and an increasing number of organizations across industries believe that smart data is more important than big data. Moreover, in the future, the most important raw material will be smart data.

For banks, smart data represents an opportunity to change the way a prospective customer’s creditworthiness is assessed, hence, a chance to expand credit to new groups of population that have previously been overlooked. In fact, financial inclusion starts with the use of smart data. While national financial institutions are looking for reasons to deny someone of access to financial services, tech companies like Smart Token Chain, BanQu and others are looking for reasons to expand connectivity and open new opportunities for those excluded from the financial system. Those companies aim to leverage a different set of records for inclusive growth and a better tomorrow.

The Anatomy of Smart Data
Mike Mondelli, senior vice president of TransUnion Alternative Data Services, listed property, tax, deed records, checking and debit account management, payday lending information, address stability and club subscriptions as some of the sources for alternative data. As he emphasized, “These alternative data sources have proven to accurately score more than 90 percent of applicants who otherwise would be returned as no-hit or thin-file by traditional models.”

Other alternative sources of data used by technology companies include web search history, phone usage, social media and more. Sources can be combined into clusters, which some professionals distinguish as traditional, social and online.


Source: Forbes, LetsTalkPayments.com

The data sources emphasized above are certainly not exhaustive and their combination can vary depending on the goal and availability. In any case, the goal is to find the most relevant, even though limited, data that corresponds with the goal of its use. Fortunately, there is a variety of fintech companies leveraging the benefits of alternative data for inclusive initiatives, credit extension and more. Such examples include ZestFinance, Affirm, LendUp—all of which use data from sources such as social media, online behavior and data brokers to determine the creditworthiness of tens of thousands of U.S. consumers who don’t have access to loans.

Companies like Lenddo, FriendlyScore and ModernLend use non-traditional data to provide credit scoring and verification along with basic financial services. Those companies are creating alternative ways to indicate creditworthiness rather than relying on traditional FICO scores. For banks, such companies open up opportunities to expand their customer base without compromising their financial returns and security, while leveraging technological advancements for adopting innovative ideas and enhancing community resilience.

How Can Your Bank Tap Into the Internet of Things?


IoT.png

The Internet of Things (IoT) has officially moved beyond hype. IoT is now well known and defined—basically putting data-gathering sensors on machines, products and people, and making the data available on the Internet—and companies are already using IoT to drive improvements in operational performance, customer experience and product pricing. Gartner predicts we’ll see 25 billion IoT data-gathering endpoints installed worldwide by 2020.

While IoT is delivering on its promise in a wide range of industries, many bankers are still struggling to find the value in finance, an industry largely built on intangibles. We see two primary IoT opportunities for banks:

  • Direct use of sensor data (location, activities, habits) to better engage customers and assess creditworthiness.
  • Partnering with companies that manufacture or integrate sensors into products to provide payment services for device-initiated transactions.

Engaging customers and assessing creditworthiness
Like most businesses, your bank can simply use IoT to understand—and serve—customers better. Banks are already implementing smart phone beacon technology that identifies customers as they walk in the door. Customers who opt in can be greeted by name, served more quickly and generally treated with more personalized care. You can also take advantage of sensor data outside of the bank to market more relevant services to customers. For example, data from sensors could alert your bank when a customer’s car goes into a repair shop; after the third service call, you might offer the customer an auto loan for a new car. This type of tailored service and marketing can change a customer’s relationship with your bank dramatically: Pleasant experiences and valued information are a time-tested path to loyalty.

IoT sensor data can also supplement traditional methods for predicting creditworthiness and protecting against fraud, especially for customers with little or no credit history. For example, if a small business HVAC contractor applies for a commercial loan, you can request access to data from shipping and manufacturing control sensors to track the flow of actual product into buildings. This can help the bank confirm how the business is doing. For product manufacturers, you can track and monitor goods, including return rates, and if the return rate is high the bank can adjust the loan pricing and decisions accordingly. Leveraging alerts on credit cards and processed payments can provide information about where and how often an individual or business is making purchases, providing clues about creditworthiness without requiring access to detailed credit card records. In short, with billions of sensors all over the world, IoT will offer you more data that can help you assess creditworthiness and prevent fraud.

Providing payment services for device-initiated transactions
To illustrate the potential of IoT, proponents often cite the “smart” refrigerator, which senses when a household is low on milk and automatically orders more. Similarly, in the commercial space, sensors can automatically trigger a call for maintenance when a piece of equipment is due for service. In these device-initiated transactions, your bank could partner with the providers to offer payment services as an integrated component of the IoT package.

On a more local level, as small businesses begin to take advantage of IoT sensors to automatically reorder supplies—paper, toner, medical supplies, salon products—your bank can tie payments into the IoT-triggered reordering system. In addition to broadening your market for payments, being part of this solution can strengthen attachment to your bank among small businesses in your community.

Start with the end in mind
This is undeniably an exciting time in banking. Between fintech offerings and IoT applications, it’s tempting to move quickly for advantage, but we all know that investments are far more likely to pay off when you treat the process with rigor and resist the urge to grab bright shiny objects. IoT is no different: Before you start buying systems and aggregating data, know what problems you’re trying to solve and what data you’ll need for the outcomes you want to achieve. In banking, the most promising returns on IoT investment are likely to be found in improved customer experiences and marketing effectiveness, reduction in loan default and fraud, and growth in your payments business. But with all the dramatic changes unfolding, who knows what innovations might be ahead—your bank might find opportunities for IoT no one else predicted.

 

Contributed by: John Matley, Principal, Deloitte Consulting LLP;Akash Tayal, Principal, Deloitte Consulting LLP;William Mullaney, Managing Director, Consulting LLP