Unlocking Banking as a Service for Business Customers

Banking as a service, or BaaS, has become one of the most important strategic imperatives for chief executives across all industries, including banking, technology, manufacturing and retail.

Retail and business customers want integrated experiences in their daily lives, including seamlessly embedded financial experiences into everyday experiences. Paying for a rideshare from an app, financing home improvements when accepting a contractor quote, funding supplier invoices via an accounting package and offering cash management services to fintechs — these are just some examples of how BaaS enables any business to develop new and exciting propositions to customers, with the relevant financial services embedded into the process. The market for embedded finance is expected to reach $7 trillion by 2030, according to the Next-Gen Commercial Banking Tracker, a PYMNTS and FISPAN collaboration. Banks that act fast and secure priority customer context will experience the greatest upside.

Both banks and potential BaaS distributors, such as technology companies, should be looking for ways to capitalize on BaaS opportunities for small and medium-sized enterprises and businesses (SMEs). According to research from Accenture, 25% of all SME banking revenue is projected to shift to embedded channels by 2025. SME customers are looking for integrated financial experiences within relevant points of context.

SMEs need a more convenient, transparent method to apply for a loan, given that business owners are often discouraged from exploring financing opportunities. In 2021, 35% of SMEs in the United States needed financing but did not apply for a loan according to the 2022 Report on Employer Firms Based on the Small Business Credit Survey. According to the Fed, SMEs shied away from traditional lending due to the difficult application process, long waits for credit decisions, high interest rates and unfavorable repayment terms, and instead used personal funds, cut staff, reduced hours, and downsized operations.

And while there is unmet demand from SMEs, there is also excess supply. Over the last few years, the loan-to-deposit ratio at U.S. banks fell from 80% to 63%, the Federal Reserve wrote in August 2021. Banks need loan growth to drive profits. Embedding financial services for SME lending is not only important for retaining and growing customer relationships, but also critical to growing and diversifying loan portfolios. The time for banks to act is now, given the current inflection point: BaaS for SMEs is projected to see four-times growth compared to retail and corporate BaaS, according to Finastra’s Banking as a Service: Global Outlook 2022 report.

How to Succeed in Banking as a Service for SMEs
There are three key steps that any institution must take to succeed in BaaS: Understand what use cases will deliver the most value to their customers, select monetization models that deliver capabilities and enable profits and be clear on what is required to take a BaaS solution to market, including partnerships that accelerate delivery.

BaaS providers and distributors should focus on the right use case in their market. Banks and technology companies can drive customer value by embedding loan and credit offers on business management platforms. Customers will benefit from the increased convenience, better terms and shorter application times because the digitized process automates data entry. Banks can acquire customers outside their traditional footprint and reduce both operational costs and risks by accessing financial data. And technology companies can gain a competitive advantage by adding new features valued by their customers.

To enable the right use case, both distributors and providers must also select the right partners — those with the best capabilities that drive value to their customers. For example, a recent collaboration between Finastra and Microsoft allows businesses that use Microsoft Dynamics to access financing offers on the platform.

Banks will also want to focus on white labeling front‑to-back customer journeys and securing access to a marketplace. In BaaS, a marketplace model increases competition and benefits for all providers. Providers should focus on sector‑specific products and services, enhancing data and analytics to enable better risk decisions and specialized digital solutions.

But one thing is clear: Going forward, embedded finance will be a significant opportunity for banks that embrace it.

Does Your Bank Struggle With Analysis Paralysis?

The challenge facing most community financial institutions is not a lack of data.

Institutions send millions of data points through extensive networks and applications to process, transmit and maintain daily operations. But simply having an abundance of data available does not automatically correlate actionable, valuable insights. Often, this inundation of data is the first obstacle that hinders — rather than helps — bankers make smarter decisions and more optimal choices, leading to analysis paralysis.

What is analysis paralysis? Analysis paralysis is the inability of a firm to effectively monetize data or information in a meaningful way that results in action.

The true value is not in having an abundance of data, but the ability to easily turn this cache into actionable insights that drive an institution’s ability to serve its community, streamline operations and ultimately compete with larger institutions and non-bank competitors.

The first step in combatting analysis paralysis is maintaining a single source of truth under a centralized data strategy. Far too often, different departments within the same bank produce conflicting reports with conflicting results — despite relying on the “same” input and data sources. This is a problem for several reasons; most significantly, it limits a banker’s ability to make critical decisions. Establishing a common data repository and defining the data structure and flow with an agreed-upon lexicon is critical to positioning the bank for future success.

The second step is to increase the trust, reliability, and availability of your data. We are all familiar with the saying “Garbage in, garbage out.” This applies to data. Data that is not normalized and is not agreed-upon from an organizational perspective will create issues. If your institution is not scrubbing collected data to make sure it is complete, accurate and, most importantly, useful, it is wasting valuable company resources.

Generally, bad data is considered data that is inaccurate, incomplete, non-conforming, duplicative or the result of poor data input. But this isn’t the complete picture. For example, data that is aggregated or siloed in a way that makes it inaccessible or unusable is also bad data. Likewise, data that fails to garner any meaning or insight into business practices, or is not available in a timely manner, is bad data.

Increasing the access to and availability of data will help banks unlock its benefits. Hidden data is the same as having no data at all.

The last step is to align the bank’s data strategy with its business strategy. Data strategy corresponds with how bank executives will measure and monitor the success of the institution. Good data strategy, paired with business strategy, translates into strong decision-making. Executives that understand the right data to collect, and anticipate future expectations to access and aggregate data in a meaningful way is paramount to achieving enduring success in this “big data” era. For example, the success of an initiative that takes advantage of artificial intelligence (AI) and predictive capabilities is contingent upon aligning a bank’s data strategy with its business strategy.

When an organization has access to critical consumer information or insights into market tendencies, it is equipped to make decisions that increase revenue, market share and operational efficiencies. Meaningful data that is presented in a timely and easy-to-digest manner and aligns with the company’s strategy and measurables allows executives to react quickly to changes affecting the organization — rather than waiting until the end of the quarter or the next strategic planning meeting before taking action.

At the end of the day, every institution’s data can tell a very unique story. Do you know what story your data tells about the bank? What does the data say about the future? Banks that are paralyzed by data lose the ability to guide their story, becoming much more reactive than proactive. Ultimately, they may miss out on opportunities that propel the bank forward and position it for future success. Eliminating the paralysis from the analysis ensures data is driving the strategy, and enables banks to guide their story in positive direction.

Recapturing the Data That Creates Valuable Customer Interactions

Before the end of 2021, regulators announced that JPMorgan Chase & Co. had agreed to pay $200 million in fines for “widespread” recordkeeping failures. For years, firm employees used their personal devices and accounts to communicate about business with their customers; the bank did not have records of these exchanges. While $200 million is a large fine by any account, does the settlement capture the true cost of being unsure about where firm data resides?

In 2006, Clive Humby coined the phrased “data is the new oil.” Since then, big tech and fintech companies have invested heavily in making it convenient for consumers to share their needs and wants through any channel, anytime — all while generating and accumulating tremendous data sets makes deep customer segmentation and target-of-one advertising possible.

Historically, banks fostered personal relationships with customers through physical conversations in branches. While these interactions were often triggered by a practical need, the accumulated knowledge bankers’ had about their customers, and their subsequent ability to capitalize on the power of small talk, allowed them to identify unmet customer needs with products and services and drive deeper relationships. Fast forward to the present day: Customer visits to branches have dropped to unprecedented levels as they embrace digital banking as their primary way of managing their finances.

But managing personal finances is different from banking. While most bank interactions revolve around checking balances, depositing checks and paying people and bills, the valuable interactions involve open-ended conversations about the desire to be able to buy a first home, planning for retirement or education, and funding large purchases like cars. These needs have not gone away — but the way consumers want to engage with their institution has completely transformed.

Consumers want to engage their banker through channels that are convenient to them, and this includes mobile messaging, SMS, Facebook messenger and WhatsApp. JPMorgan’s bankers may not have been trying to circumvent securities regulations in engaging with customers on their terms. Failing to meet your customers where they are frustrates both customers and bankers. Failing to embrace these digital channels leads to less valuable data the bank can use.

Banking platforms — like digital, payment and core banking — can capture data that provides insight into consumers’ saving and spending behavior, but fails to capture latent needs. Institutions that make it more convenient for customers to ask their personal banker something than Googling it opens up an entirely new data source. Allowing customers to ask open-ended questions augments transactional insight with unprecedented data on forward-looking needs.

In a recent case study, First National Bank of Omaha identified that 65% of customers expressed interest in exploring new products and services: 15% for credit cards, 12% for home loans, 9% for investments, and 7% for auto loans.

If “data is the new oil,” the real value lies is in the finished product, not the raw state. While data is exciting, the true value is in deriving insights. Analyzing conversational data can provide great insight. And banks can unlock even greater value when they analyze unprocessed conversational data in the context of other customer behavior, like spending patterns, propensity to use other engagement channels and socio-demographic changes.

At present, most of this data is owned and guarded by financial processors and is not readily available for banks to access and analyze. As banks extend their digital engagement model, it is imperative they own and can access their data and insights. And as banks increasingly see the benefits of allowing customers to engage with their banker in the same way they talk to their friends, key considerations should include:

  • Conversation aggregation. Is a customer’s conversation with multiple bankers aggregated to a single thread, avoiding data lost through channel switching?
  • Are conversations across channels retained within a dedicated and secure environment?
  • Can conversations transition from one relationship banker to another, avoiding the downfall of employee attrition?
  • Are suitable tools powered by artificial intelligence and other capabilities in place to ensure a real-time view of trending topics and requests?
  • Data access. Is raw conversational data readily available to the bank?

Engaging customers through digital channels presents an exciting opportunity for banks. No longer will data live within the mind of the banker: rather, insight that are derived from both individual and aggregate analysis can become a key driver for both strategic and tactical decisioning.

The Future of Fighting Financial Crime

The anti-financial crime landscape is continuously evolving, and financial institutions need to stay a step ahead of emerging fraud trends and regulatory compliance challenges to protect their customers and themselves from loss and reputational damage.

As consumers become increasingly reliant on the speed and convenience of digital banking products, institutions should consider end-to-end financial crime management solutions that offer real-time fraud detection, targeted AML transaction monitoring and automated regulatory reporting to fight financial crime and strengthen compliance. With artificial intelligence, including machine learning and robotic process automation (RPA), behavior-based anti-financial crime management solutions can help institutions increase the effectiveness and efficiency of their fraud and AML programs.

But artificial intelligence relies on the power of big data. Anti-financial crime management solutions need an immense data set from multiple sources, including core, ancillary, open-source, third-party and consortium data. Artificial intelligence can be applied to this data with cross-institutional analysis in a cloud-based environment. Solutions built with big data and artificial intelligence reduce false positives and increase the quality and accuracy of alerts.

Analytical agents built with machine learning algorithms continuously analyze data to improve analytical performance. A large, cross-institutional data set in a cloud-based environment allows machine learning agents to train on labeled data from thousands of institutions, achieving performance levels that cannot be matched by a single institution with a limited, isolated and restricted data set. Machine learning can significantly improve analytical performance, helping institutions reduce false positives and reduce the alert review time to increase the efficiency of investigations, while continuing to detect new and emerging criminal trends.

Machine learning agents use mathematical and statistical models to learn from data without being explicitly programmed. These agents analyze new data, including transactions, demographics and customer behavior, and utilize this evidence in transaction monitoring alerts. The alerts provide feedback to the cloud-based data set, where the training data, which includes the transaction, demographic and customer-behavior evidence, and labeled data, such as cases, marked transactions and return items, are input to a machine learning algorithm.

This data is used to train many different types of machine learning agents to determine which type of agent performs best for a particular typology. Before training an agent, the data is split into training, testing and validation data sets, so that the results of the training can be validated in an unbiased manner. Anti-financial crime management solutions can use precision, recall and false positive rate to validate analytical performance and assign the most suitable analytical agent to a particular fraud or money laundering typology.

Strengthening Processes with Robotic Process Automation

Institutions can leverage technology such as RPA to improve internal processes, strengthen anti-financial crime management programs and ensure regulatory compliance. Using RPA to improve workflow automation can save financial crime investigators time by reducing manual tasks, automating steps in alert triage and regulatory reporting processes, and through prepopulating and submitting reports with consistency, speed and accuracy.

Enhanced data collection through RPA reduces human error that occurs during manual data collection and transference. It also automatically and reliably integrates multiple data sources in your anti-financial crime management and compliance solutions.

Anti-financial crime management programs can use RPA agents to validate information populated in a currency transaction report or a suspicious activity report, automatically submit reports, intelligently package related alerts together, and automatically assign work to a team or an investigator. Such solutions can also automatically triage alerts and segment customers into appropriate risk categories, increasing the efficiency and effectiveness of financial crime investigations.

To stay ahead of financial crime trends, financial institutions should consider the benefits of cloud-based solutions that leverage artificial intelligence to increase the effectiveness and efficiency of anti-financial crime management and compliance programs.

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


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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


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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


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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.