The biggest banks are spending billions on technology, but community banks can level the playing field by choosing technologies that personalize and enhance their interactions with customers, as Michael Carter, executive vice president at Strategic Resource Management, explains in this video. He shares how data and voice-enabled technologies could help community banks provide the digital experience that customers want.
Leveraging Data to Enhance the Customer Experience
Fintech companies are laser-focused on improving consumer engagement—but there is room for traditional banks to gain ground, according to Craig McLaughlin, president and CEO of Extractable. In this video, he shares three ways banks can strategically approach improving the customer experience at their own institutions.
Advanced data science technologies like artificial intelligence (AI), machine learning and robotic process automation are delivering significant benefits to many banks.
As part of their mandate to protect shareholder value and improve financial performance, bank directors can play an important role in the adoption of these promising new technologies.
Technology’s expanding influence With fintech companies generating new competitive pressures, most traditional banks have recognized the need to adopt some new techniques to meet changing customer habits and expectations. Declines in branch traffic and increased online and mobile banking are the most obvious of these trends.
Yet, as important as service delivery methods are, they are in a sense only the top layer of bigger changes that technology is bringing to the industry. New data-intensive tools such as AI, machine learning and robotic process automation can bring benefits to nearly all areas of a bank, from operations to sales and marketing to risk and compliance.
Advanced data analytics can also empower banks to develop deeper insights and make better, more informed strategic decisions about their customers, products and service offerings.
The power of advanced analytics Historically, business data systems simply recorded and reported what happened regarding a customer, an account, or certain business metrics. The goal was to help managers understand what had happened and develop strategies for improving performance.
Today’s business intelligence systems advance this to predictive analysis – suggesting what is likely to happen in the future based on what has been observed so far. The most advanced systems go even further to prescriptive analysis – recommending or implementing actions that increase or decrease the likelihood of something happening.
For example, AI systems can be programmed to identify certain customer characteristics or transaction patterns, which can be used for customer segmentation. Based on these patterns, a bank can then build predictive models about those customer segments’ likely actions or behaviors – such as closing an account or paying off a loan early.
Machine learning employs algorithms to predict the significance of these customer patterns and prescribe an appropriate response. With accurate segmentation models, a bank can tailor marketing, sales, cross-selling and customer retention strategies more precisely aligned to each customer.
Automating these identification, prediction, and prescription functions frees up humans to perform other tasks. Moreover, today’s advanced analytics speed up the process and can recognize patterns and relationships that would go undetected by a human observer.
Industry leaders are using these tools to achieve benefits in a range of bank functions, such as improving the effectiveness of marketing and compliance functions. Many large banks already use predictive modeling to simplify stress testing and capital planning forecasts. AI and machine learning technology also can enhance branch operations, improve loan processing speeds and approval rates and other analytical functions.
Getting the data house in order While most banks today are relatively mature in terms of their IT infrastructures and new software applications, the same levels of scrutiny and control often are not applied to data itself. This is where data governance becomes crucially important – and where bank directors can play an important role.
Data governance is not just an IT problem. Rather, it is an organization-wide issue – and the essential foundation for any advanced analytics capabilities. As they work to protect and build shareholder value, directors should stay current on data governance standards and best practices, and make sure effective data governance processes, systems and controls are in place.
AI, machine learning, and robotic process automation are no panacea, and banks must guard against potential pitfalls when implementing new technology. Nevertheless, the biggest risk most banks face today is not the risk of moving too quickly – it’s the risk of inertia. Getting started can seem overwhelming, but the first step toward automation can go a long way toward taking advantage of powerful competitive advantages this technology can deliver.
Today, data helps competitive banks identify key targets and make smarter—and quicker—loan decisions. In this video, Bill Phelan, president of PayNet, explains how data analysis is shifting loan decisioning, and how banks can survive and thrive through the next credit crisis. He also shares his outlook for business lending, and believes that Main Street America is still looking for capital to grow and improve their businesses.
Data analytics affects all areas of the bank, from better understanding the customer to addressing regulatory issues like stress testing. However, organizations face several barriers that prevent unlocking the power of predictive analytics. John Sjaastad, a senior director at SAS, outlines these barriers, and shares how bank management teams and boards can address these issues in this video.
It’s no secret that no matter the business, access to the right data at the right time can provide valuable insights into the current state of that business and potentially an entire industry and its future.
Accurate, real-time data serves as benchmark against past performance while also providing a roadmap for future trends. Access to the right information could be the key to preventing small issues from becoming multi-billion or even trillion-dollar problems.
During the global financial crisis, the International Monetary Fund estimated that banks and other financial institutions faced aggregate losses of $4.05 trillion, $2.7 trillion of which came from loans and assets originating in the United States, according to The New York Times. Additional news outlets, including the Wall Street Journal, have reported total global losses as large as $15 trillion from the crisis. Imagine if just a fraction of these losses could have been avoided with timely access to critical data insights.
Within an industry that has remained confined to spreadsheets and paper files, the real value in digitization is the data modern technology can unlock. Too many decisions in the construction lending industry are being made with outdated information and in some cases, purely by instinct. With so much data available to us, why isn’t every decision data-driven?
Software Can Help Streamline Processes The traditional construction lending administration process requires loan administrators to manually gather data from various spreadsheets and paper files when assembling reports.
This manual and time consuming process costs credit departments days, and sometimes weeks of valuable time, and as a result, many financial institutions only review comprehensive portfolio data when it is deemed absolutely necessary. Put aside for a moment the high percentage of human errors found in everyday spreadsheet reporting, this lack of oversight leaves lenders vulnerable to compliance issues and unexpected risk, which can easily be avoided with proper reporting and analytics platforms.
Mitigate Risk with Data Despite the earning potential of construction portfolios, lenders and credit management departments typically avoid construction loans because they are often considered the riskiest loans within a bank’s portfolio, garnering significant attention from regulatory agencies to ensure risk is being managed properly. Construction lending software allows financial institutions to leverage the growing construction market without absorbing the additional risk. Digitization provides financial institutions complete reporting capabilities and unprecedented portfolio insights, giving lenders the ability to readily access complete reports about an array of issues including rate and fee variances, inactive or stale loan accounts, matured loans, liens and insurance lapses, among others.
Up to date and readily available reporting and portfolio insights allow lenders to quickly identify potential issues, significantly reducing the inherent risks associated with the complex nature of construction lending.
Satisfy Auditors and Examiners Limited inventory, especially in entry-level housing, and increased demands within the housing market have resulted in a continuation of the national housing shortage in 2018. With an influx in first-time homebuyers expected this year, experts have predicted significant growth in the construction industry, including a 9-percent increase in single-family housing starts.
As a result, examiners will be paying closer attention to swiftly growing construction portfolios in order to ensure regulatory compliance. Construction loan automation software allows lenders to better prepare for compliance exams with easily accessible reports that provide examiners the information they require with little interruption to a credit management department’s daily workflow.
Drive Your Decisions with Data Through digitization, financial institutions have the ability to quickly and easily pull both global and granular reports from their entire construction portfolio, allowing lenders to use reporting capabilities to create insightful metrics that can be applied to performance tracking, accounting strategies, and strategic planning. In construction lending, reporting of this caliber allows lenders to make data-driven decisions by identifying, measuring, and tracking effective solutions, while eliminating or improving failed strategies.
A 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.
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.
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.
Social media allows banks to appeal and engage with millennials, who constitute a quarter of the U.S. population.
Banks are actively stepping into the social media game by creating Facebook pages, Twitter accounts and YouTube channels to reach the masses with company updates, money management tips and education. IBM suggests that banks need to use social media not only for outreach—but customer service as well. The tech giant notes that millennials are more apt to air their grievances via social media than call a bank directly and wait on hold. Banks can use their twitter accounts as a customer sounding board and to address issues directly?thus, keeping customers happy and their money in the bank.
Social Media Data for Underwriting It is projected that in 2015, there were 26 million credit invisible consumers in the United States alone. About 8 percent of the adult population in the country have credit records that can’t be scored based on a widely used credit scoring model. Those records are almost evenly split between the 9.9 million that have an insufficient credit history and the 9.6 million that lack a recent credit history.
While large financial institutions are heavily focused on serving the credit-eligible population across the country, community banks play a critical role in the welfare of those left beyond the borders of eligibility. The opportunity to expand access to financial services in communities with an ineligible population is a critical step towards financial inclusion in those communities.
Social media channels are gaining an important role as alternative sources of data on credit eligibility. Who you know matters (especially in defined communities), and companies like Lenddo, FriendlyScore, ModernLend and credit scoring solution providers are leveraging this idea with the use of non-traditional sources of data to provide credit scoring and verification along with basic financial services. Social media also gives lenders an insight into how an applicant spends their time, which can be used as an alternative way to indicate someone’s financial trustworthiness, expanding opportunities for banks to reach new categories of customers.
While loan officers at megabanks apply impersonal qualification criteria without regard to individual circumstances, community banks are initially better positioned to benefit from the use of social media channels to get to know their customers even closer than they already do.
As emphasized by the team at Let’s Talk Payments, a source of information and research online about emerging financial services and payments, the following are some of the tangible opportunities for banks embracing social media data for creditworthiness assessment:
The opportunity to capture a new customer segment
Differentiated customer experience
Strengthening the existing underwriting process
Enhanced fraud prevention
Stronger engagement with the community
Given the scale of credit invisibility in the country, an innovative approach to potential customer profiling in communities where banks operate could serve as a competitive edge for those banks. Social media data can be used to extend loans to previously ignored groups in the population, improving household resilience and building stronger ties between community banks and their immediate communities.
Social Media is About Relevancy and Accessibility There are two elements to relevancy and accessibility: an opportunity to gather feedback to improve products and services and the opportunity to increase accessibility and transparency to customers.
“Customer feedback is indispensable for any business that wants to grow, and the same holds true of community banks. Social media interactions are your doorway to customer conversations and feedback, which can help you fine tune your business. Tapping into online conversations on social media should shed light on customer problem points, helping resolve issues before they escalate,” said Jay Majumdar, vice president of sales at ICUC, a social media management services company.
Ignoring conversations about your bank on social media is a dangerous path—it removes control over the message and brand image, and it damages your reputation as a customer-centric business. It’s especially damaging for community banks that are dependent on community loyalty and long-standing relationships with customers. Jill Castilla, president and CEO of Citizens Bank of Edmond, echoes this point, saying that “social media is not about putting a message out there and leaving it. It’s a conversation.” She also emphasizes that “social media is about relevancy and the accessibility that you expect from your hometown community bank. It’s a tangible way that our community can see we’re living up to be the community bank you used to think about. That’s what social media allows us to achieve.”