Tag: AI

Innovation in fraud detection and prevention is key in today's ever-evolving digital landscape. Juniper Research, a research firm that specializes in identifying and appraising new high growth market sectors, recognized organizations and platforms that drive innovation and growth in the banking, fraud and security, and retail and payments through their Future Digital Awards. The firm awarded Experian as the Platinum Winner for Fraud Detection and Prevention Platform (CrossCore™) and the Gold Winner for the Artificial Intelligence Platform (Ascend Intelligence Services™). Keeping more consumers safe According to this year's Global Identity and Fraud Report, more than half of businesses will continue to invest in fraud prevention solutions over the coming year to combat several types of fraud: new account opening fraud, account takeover fraud, and other types of identity fraud, with at least 57 percent of businesses report higher losses from account opening and account takeover fraud. Identity-related fraud has evolved towards more automation, in the form of scripted attacks and bot attacks, as well as more sophisticated phishing attacks. The speed at which fraudsters adapt to new technology and behavior has always been a problem, and with sudden and unpredictable change, reacting quickly with new fraud strategies has never been more important for businesses looking for ways to safeguard digital transactions. CrossCore™, launched in 2016, is used globally to connect identity and fraud capabilities. The system combines robust risk-based authentication, identity proofing and fraud detection into a single, state-of-the-art cloud platform to make real-time risk decisions throughout the customer lifecycle. Typically, businesses need to move through validation, contract and then integration in order to combat fraud – making for a long, tedious and expensive process. CrossCore pre-qualifies fraud and intelligence services so that businesses can choose how they want their transactions to be processed and which fraud and identity services they want to use. The platform is designed to help businesses instantly identify good customers, catch fraud and enhance the customer experience. Juniper Research’s Future Digital Awards for Fintech & Payments recognized Experian’s CrossCore as the Platinum Winner for the Fraud Detection and Prevention Platform. The recognition comes at a time CrossCore and AIS platforms are helping businesses all over the world combat fraud and maintain a safe digital experience for their customers. This recognition underscores the commitment to using advanced capabilities in data, analytics and technology to bring innovative fraud solutions to the market, enabling businesses outpace fraud while making it safer for consumers to engage with them digitally. Providing better digital service The acceleration to digital has caused financial institutions to quickly evolve and improve their processes including reducing time for loan approvals, access to more financial produce and new innovative payment methods. What is most important is that businesses focus on more on advanced technologies for lending. Launched in January 2021, AIS provides financial institutions and other lenders with AI solutions delivered rapidly and digitally, resulting in better business outcomes at every stage of the customer lifecycle. AIS is a one-stop-shop of building, documenting, deploying, monitoring, and retraining analytics, all on the same AI platform. The system allows businesses to process data with extreme speed and efficiency in a streamlined approach to detect and monitor identity models and strategies. Juniper Research’s Future Digital Awards for FinTech & Payments also recognized Ascend Intelligence Services™ (AIS) as the Gold Winner for the AI Platform. By creating accessible AI solutions for our business clients, people engage with their favorite financial brands in a more meaningful way across the customer lifecycle, truly democratizing advanced analytics. Learn more about Ascend Intelligence Services and CrossCore.   Stay in the know with our latest research and insights:

Getting the most out of your AI investment Work backward from impact - give yourself room to experiment Hire the best data talent and partner with the right provider Take a holistic approach - don't just focus on performance AI allows businesses to process sheer volumes of data and multi-tiered models with extreme speed and efficiency. But, scaling AI to meet shifting business demand can be challenging. Experian's Ascend Intelligence Services expertly partners with organizations to build custom, scalable AI and ML solutions to meet those requirements. Listen to Shri Santhanam advise on how to scale AI

Why digital acceleration has created more opportunities for deepfake fraud tactics like voice cloning and what businesses can do about it Digital acceleration has placed information and services in the hands of the masses, connecting individuals on a global level like never-before, and in turn making them increasingly dependent on devices in their daily lives. The argument for technology as an equalizer in society is a strong one. Most people have a voice and a platform, producing millions of virtual interactions and recordings every day. But in this digital world of relative anonymity, it is difficult to know who is really on the other side of the connection. This uncertainty gives fraudsters an opening to threaten both businesses and consumers directly, especially in the realm of deepfakes. What is a deepfake? Deepfakes are artificially created images, video and audio designed to emulate real human characteristics. Deepfakes use a form of artificial intelligence (AI) called deep learning. A deep learning algorithm can teach itself how to solve problems using large sets of data, swapping out voices and faces where they appear in audio and video. This technology can deliver extraordinary outcomes across accessibility, criminal forensics, and entertainment, but it also allows a way in for cybercriminals that hasn’t existed until now. Deepfake fraud tactics A principal tactic among deepfake fraud is voice cloning – the practice of taking sample snippets of recorded speech from a person and then leveraging AI to understand speech patterns from those samples. Based on those learnings, the modeler can then use AI to apply the cloned voice to new contexts, generating speech that was never spoken by the actual voice owner. For businesses, deepfake tactics such as voice cloning means access to points of vulnerability in authentication processes that can put organizations at risk. Fraudsters may successfully bypass biometric systems to access areas that would otherwise be restricted. For government leaders, it can mean the proliferation of misinformation – a growing area of concern with huge repercussions. For consumers, the risk of falling victim to scams involving access to personal information or funds is particularly high when it comes to voice cloning. How to prevent deepfake fraud 1. Vigilance: Stay on top of sensitive personal information that could be targeted. Fraudsters are always at work, relentlessly seeking out opportunities to take advantage of any loophole or weak spot. Pay close attention to suspicious voice messages or calls that may sound like someone familiar yet feel slightly off. In an era of remote work, it is important to question interactions that can impact business vulnerabilities – could it be a phishing or complex social engineering scam? 2. Machine learning and advanced analytics: Deepfake fraud is an emerging threat, which leverages the development and evolution of the technology that fuels it. The flip side is that businesses can in fact use the same technology against the fraudsters, fighting fire with fire by deploying deepfake detection and analysis. 3. Layered fraud prevention strategy: Leveraging machine learning and advanced analytics to fight deepfake fraud can only be effective within a layered strategy of defense, and most importantly, at the first line of defense. Ensuring that the only people accessing the points of vulnerability are genuine means using identification checks such as verification, device ID and intelligence, behavioral analytics, and document verification simultaneously to counter how fraudsters may deploy or distribute deepfakes within the ecosystem. As with many types of fraud, staying one step ahead of the fraudsters is critical. The technology and the tactics continually evolve, which may make the countermeasures on the table right now obsolete, however the fundamentals of sound risk management, with the right layered approach, and a flexible and dynamic solution set, can mitigate these emerging threats.   Stay in the know with our latest research and insights:

How elite leaders train analytics teams to unearth and convey the highest quality data insights and better manage risk. It's surprising how much of an art the effective use and analysis of qualitative data in the business world truly is. Too often, data scientists are tasked with turning raw data into insights without ever actually being taught the true art of identifying and reporting the most meaningful insights that address the problems at hand. Instead, data teams often produce reams of summarized information without drawing any useful conclusions – falling short of discovering deeper truths hidden within. I've been fortunate to work for, with, and manage data scientists of various titles, abilities, and personalities over the years. I've found that the true "artists" in this profession can combine technical proficiency, tactical communications with an affinity for the science, and excellent detective skills. Objectivity in Data Analysis As Arthur Conan Doyle wrote in Sherlock Holmes says, "I never guess. It is a capital mistake to theorize before one has correct data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts." As data scientists, we're often sent down a singular path to analyze data to support a narrative. Data is inherently objective; analyzing with subjective intent typically leads to ineffective results when put into practice. However, with the proper guidance, probing questions, and some detective work, scientists can uncover deeper insights leading to effective outcomes in the form of actionable intelligence and forecasts. Early in my career, I was tasked by a business partner to pull data that demonstrated higher customer satisfaction scores for a customer call center. Requests like this – "just get me the data" – are (unfortunately) common. In this case, however, he was open to discussing the "why" behind his ask. As a result, this incident proved a learning opportunity for me on how to satisfy a requirement while simultaneously producing information explicitly valuable to the organization. I've often had to find workable paths through figurative minefields with mandates such as "just get me the data" or "make the numbers work." During this scenario, I diligently asked ancillary questions to build into the data modeling outside the required parameters. I intended to generate value beyond the pre-conceived conclusion I was tasked with finding data for. The resulting report yielded compelling insights, actionable intelligence, and a clear forecasting plan. In this example, it was found that clients had higher satisfaction scores for reasons other than what we initially thought and had nothing to do with the seven million dollars my business partner spent on branding, training, etc. The solution was simple: move a training location. Tactical communication skills were necessary in this scenario as I had to tell my business partner where the efficiency gains were actually coming from and where future budgets could be more effective. Doing so was the catalyst behind an alternative business strategy and focus, resulting in a much more significant impact on our customer relationships. Asking the Right Questions The true purpose of analytics is to discover, interpret, and communicate meaningful patterns in data and the connective tissue between. Most importantly, it exists to aid in effective decision-making within an organization. Under that premise, I teach my teams to be communicative, especially during planning stages and consistently ask questions of the data throughout the analytical process. It's always imperative to identify the specific addressable problems our clients are trying to solve while frequently conversing with them to understand what actions and/or decisions the analysis is meant to inform. This strategy produces more profound results and focuses on solving a problem – not endlessly cycling through various cuts of the same data. As a result, the team will be primed to evaluate results objectively and be ready to dig beyond surface-level data, capturing vital insights hidden deep within. Using the Right Tools Nobody does arithmetic by hand anymore. A data scientist's best friend should be sophisticated model development software that leverages AI and Machine Learning. The efficiency they provide enables us to focus on areas where human intelligence is best applied, such as interpreting model performance within the context of how that model will be used. Elite leaders know how to leverage the right tools to maximize speed and efficiency. Ignoring the sheer processing power of cloud computing and other advancements places your organization at a distinct competitive disadvantage in performance and accuracy. I shudder when thinking about the dark days when it would take six to nine months to develop a new model. It reminds me of watching NASA mathematicians do advance calculations with slide rules in movies like Apollo 13 and Hidden Figures. Strategy optimization is a perfect example; how do I ensure that my portfolio is holistically delivering the highest value within risk constraints? I could grow my portfolio endlessly, but that likely means taking on too much back-end risk. Instead, mathematical optimization can be used to determine the right balance between growth, return, and risk. To do this successfully requires a vast amount of processing power. Gradient boosting, a Machine Learning technique that helps build far more accurate models, is another excellent example of what's possible with modern technology. Some of the operations we perform daily were literally not possible 10-15 years ago as we did not have access to such processing power. Thus, we're able to solve problems not previously solvable. What has also changed is our ability to process volumes of data and highly complicated, multi-tiered models, with extreme speed and efficiency. Organizations don't need to take all of this on, as companies like Experian effectively provide data science services where AI/ML solutions are delivered rapidly and digitally. A well-equipped, efficient, curious, and well-trained data team whose data analysis consistently helps corporate leaders make informed decisions is true art. The answers they provide to challenging business questions is their magnum opus. Read about topics related to this article Stay in the know with our latest research and insights:

Shri Santhanam, EVP and Global Head of Analytics and AI, talks to Ganesh Padmanabhan from Stories in AI about why he hopes the changing world of lending will lead to better financial inclusion. "The whole digital revolution in lending means that financial institutions are scrambling to make the process much more seamless, reduce time for approvals, let consumers have access to different financial products, and have innovative products like buy now pay later. But underneath it all, you have to get more nuanced and more sophisticated about the methodologies that you use for lending. And this is where AI and ML come in." Expect to hear discussions about the future of finance, how to drive impact by leveraging data analytics and AI, frameworks for setting up and institutionalizing an AI center of competence for a large organization, and how to scale data science efforts through hiring, promoting from within, and setting up the right structure and processes to make it happen. "Experian for over 100 years now has leveraged the power of data. We’ve been a very powerful data company. We’ve used that data to improve the lives of consumers and improve how businesses make decisions. Fundamentally, we’ve had a set of pioneers who before Big Data tech was introduced to the world, figured out that having a data marketplace or collecting high quality data on consumer lending will be of value, and that’s been the core of our business. That dynamic is changing. We see a lot of value migrating what we call up the stack. So from purely data to actually the decisions that are made with the data, to products and services in the data."   Related content

Experian is honored to be recognized as a winner of the Artificial Intelligence AI Excellence Award by Business Intelligence Group. Experian was recognized for its credit and collections decisioning solution, PowerCurve, which features intelligent agent-customer assist that processes complex, regulated, and subjective interactions with customers, revolutionizing how they digitally interact, on their terms, with lenders. This AI virtual assistant offers customers 24/7 access to support from their credit provider - on a financially sensitive transaction such as collections. We embedded over 30 years of collections knowledge and experience delivered to major clients across the globe into a credit risk decision management solution that delivers this domain expertise, data, analytics, decisioning, and workflow to clients of all sizes via cloud technology. Harnessing the power of AI has enabled lenders to easily connect with customers on their terms in a fair and transparent way, and to recover losses in an operationally efficient way but also improve customer satisfaction. When’s the last time you heard a customer share a positive story about collections?  This digital self-service channel allows customers to engage with a lender on their terms and allows the lender to fulfill key objectives for quickly recovering losses, using the company’s strategic parameters for segmentation and treatment paths. The intelligent agent customer-assist feature within PowerCurve continuously learns which distinguishes it from the traditional decision-tree structure of a chatbot. It remembers interactions and learns from them, has short-term and long-term conversation goals, and recognizes small talk. It treats customers fairly and transparently. The result feels more empathetic and allows for an always-on and real-time consumer interaction. It can also offer a future-forward benefit to lenders, by allowing their employees to strategically focus on other areas of the business, potentially increasing the capacity for more credit products and services innovation. This Business Intelligence Group awards program sets out to recognize those organizations, products and people who bring Artificial Intelligence (AI) to life and apply it to solve real problems. Related stories: Going the last mile: Improving the End-to-End Digital Customer Journey Technology today: Focus on innovation drives award-winning, AI-powered consumer lending Podcast: Driving product vision with the customer front and center, a software architect’s view

As consumers shop, bank, and pay online during the global pandemic, steps are being taken to return to more business as usual. But, what should businesses expect around consumer digital preferences post-pandemic? Steven Wagner, Global Managing Director of Decision Analytics, recently spoke with Jill Malandrino of Nasdaq Trade Talks about recent survey findings and overall trends to watch out for. Here are highlights of that discussion: Research trends indicate a continued and persistent surge in online transactions and digital payment mechanisms The current environment has been a tipping point for consumer trust in online transactions A secure environment through continuous and passive authentication is key to meeting online consumer demand There is a direct correlation between consumer trust in their online environment and their willingness to provide data to secure a transaction Businesses need to invest in AI that provides a good consumer experience -- from chatbots to machine-learning models that impact consumer treatment in real-time Watch the full episode now: Related stories: New research available: 2021 Global Insights Report Parting ways with old forms of managing credit risk online Why the new era of customer experience includes passive authentication

Given market dynamics, technology today is an important topic for businesses to track — whether beginning a technology transformation journey or looking to continuously improve by understanding the latest advancements available — including AI-powered consumer lending. With innovation at the forefront of practices, our experts help power important topics around this subject — and fuel breakthrough technologies. Recently, this focus on innovation was acknowledged, with Ascend Intelligence Services™, winning both honors from The CIO 100 Awards and The FinTech Breakthrough Awards, premiere and notable programs that recognize breakthrough technology worldwide. ML technology helps drive customer satisfaction and increased bookings The CIO 100 Awards recognized Atlas Credit, a midsized lender headquartered in Texas for their use of the Experian Ascend Intelligence Platform. The platform enabled them to double loan application acceptance rates while reducing credit losses by up to 20 percent. Atlas Credit uses the tools and data to make instant decisions, resulting in improved customer satisfaction and higher booking rates. Using Ascend Intelligence Services, Experian data scientists rapidly built a machine learning (ML) custom credit risk model, optimized a decision strategy, and deployed the model in production, reducing time to impact by six months. “Winners are chosen by a team of external judges, many of them former CIOs, on their use of leading-edge IT practices that produce measurable results. The award is an acknowledged mark of enterprise excellence. This year's honorees exemplify what it means to deliver business value through the innovative use of technology. This elite group is creating competitive advantage in their organizations, improving business processes, enabling growth and improving relationships with customers," according to CIO 100 2021 winners. The CIO 100 awards are presented by IDG's CIO — the executive-level IT media brand providing insight into business technology leadership, each year, CIO recognizes the premier organizations and executives driving IT innovation with these prestigious awards. Honorees are inspiring examples of how IT leadership, business partnerships, and customer engagement are reshaping the future. Atlas Credit was the recipient of the Optimal Loan Underwriting with Machine Learning for Underserved Consumers project. Better results, delivered faster to help power FinTech evolution Experian's Ascend Intelligence Services was also selected as a winner of The FinTech Breakthrough Honors, which recognizes Standout FinTech Companies and Solutions in 2021. Ascend Intelligence Services was recognized as the FinTech Breakthrough Award winner in the Consumer Lending Innovation Award category. The FinTech Breakthrough Awards is the premier awards program founded to recognize the FinTech innovators, leaders, and visionaries from around the world in a range of categories, including Digital Banking, Personal Finance, Lending, Payments, Wealth Management, Investments, RegTech, InsurTech, and many more. The 2021 FinTech Breakthrough Award program attracted more than 3,850 nominations from across the globe. "This past year has been unlike any time period ever seen before for FinTech growth and disruption, with FinTechs maturing to become respected, global players throughout the financial services value chain," said James Johnson, Managing Director, FinTech Breakthrough. "FinTech is a digital force that has clearly entered a new phase of its evolution, moving out of niche use cases to operate at scale, and we are thrilled to recognize the 'breakthrough' FinTech innovators in this market evolution for our fifth annual FinTech Breakthrough Awards program." Ascend Intelligence Services is a fully managed analytics service delivered digitally by Experian data scientists. It helps lenders leverage advanced technology, utilizing AI to deliver better results up to 5x faster, accelerating time-to-market all while allowing them to make sound business decisions. Related stories: Insights in Action Podcast: Identifying the core capabilities your business needs to get MLOps right Fair and explainable artificial intelligence is accelerating industry transformation Going the last mile: Improving the End-to-End Digital Customer Journey

Did you miss these February business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. Experian launches new anti-fraud platform for digitally accelerated world Financial IT covers the latest on tools to help businesses safely meet the rapid increase in demand for digital services and online accounts. Eduardo Castro, Head of Identity & Fraud, speaks to gaining confidence in preventing fraud while meeting these new business challenges. Experian helps Atlas Credit double approval rates while reducing credit losses by up to 20 percent This Global FinTech Series article provides insights on efforts to make the power of artificial intelligence accessible for lenders of all sizes. Shri Santhanam, Executive Vice President and General Manager of Global Analytics and AI, shares background on constantly-changing economic conditions impacting credit models and how to rapidly develop and deploy models to keep up. 60 percent of consumers are using a universal mobile wallet New research shows a continuing trend toward digital transactions and mobile wallet payments. Steve Wagner, Global Managing Director of Decision Analytics, speaks to consumer and business insights on the increased demand and what businesses need to consider to ensure positive customer journeys that support these shifts. Why digital identity and the customer journey is crucial for today’s businesses Steve Pulley, Managing Director of Data Analytics, explores business opportunities stemming from the massive increase in consumers accessing services online. Taking the right steps not only helps ensure business survival but sustainable success. The key is fundamentals including the customer journey and digital identity. How modern data strategies underpin the digital identity and authentication practices critical to digital transformation In this Datanami article covering our progression toward a 'contactless world,' modern fraud prevention is explored. Dealing with a tremendous amount of data to offer security, while bearing in mind customer convenience, requires sophisticated technology. Holistic approaches both improve operations and helps keep pace with fraudsters to protect customers. Stay in the know with our latest insights:

Artificial Intelligence (AI) offers people and companies many advantages, and we interact with it every day. From the technology we use to do simple things like heating and cooling our homes, to more advanced tools that map potential disease outbreaks across the globe. AI is also being used more and more in the financial services sector – from matching new customers with the right loan and terms to assisting with transactions in real-time online. In a recent study, we found two-thirds of businesses surveyed globally are using AI to help manage their businesses today. More businesses are keen to use AI but are challenged to fulfill requirements for decision explainability – a must-do for ensuring consumers are treated fairly. The history of AI AI stems from the realization of the potential of computation. The father of theoretical computer science and AI, Alan Turing, introduced a theoretical mathematical model of computation – aptly named the Turing Machine – in 1936. He described this machine as being capable of computing anything computable. By 1950, his work posed the question “Can machines think?” He introduced the Turing Test, still in use today to subjectively evaluate whether a machine is intelligent based on its ability to have a conversation. Six years later, in 1956, prominent computer scientists proposed the famous Dartmouth Summer Project. Advanced concepts were introduced and discussed and the term "artificial intelligence" was first coined. Over the following two decades, AI flourished. Computers became not only faster, cheaper, and more accessible, but they were progressively able to store more information. Meanwhile, machine learning algorithms continued to improve, getting the interest of experts in different fields and industries and taking the realm of artificial intelligence to a tipping point in the early ’80s. Back then, John Hopfield and David Rumelhart popularized “deep learning” techniques which allowed computers to learn from experience. Meanwhile, Edward Feigenbaum introduced expert systems which mimicked the decision-making process of a human expert, allowing the program to ask an expert in a field how to respond in a given situation and to learn from it. How can AI benefit both businesses and consumers? Following these early milestones, the advanced analytics sector has experienced explosive growth – with AI impacting many aspects of our lives today. While most people have come to realize that AI can be beneficial, even since the early days, there have been many different views on how those involved in programming the algorithms must take the necessary steps to prevent AI from reinforcing stereotypes, widening wealth and educational gaps, or providing incorrect answers at critical junctures such as in a medical setting. As an example of what not to do: a famous language model was trained using 8 million pages sourced directly from the web. So, implicit in this model are the preconceptions and biases included in its training data. In this case, it led to a model with a trend towards greater male bias in more senior, higher-paying jobs. How to determine fairness in AI models So how can we ensure that the use of AI does not reinforce societal racism, sexism, or other stereotypes? That leads us to define fairness. It’s the impartial and just treatment of people without favoritism or discrimination; when no unjustified distinctions occur based on groups, classes, or other categories to which they are perceived to belong. But, within the world of AI, there are varying approaches to fairness associated with different metrics to evaluate and adopt this sought-after algorithmic fairness. Any solution requires defining dimensions of fairness, but realistically, it’s extremely hard to capture all these very sensitive variables and risky to store and process them. To truly determine if an AI system is fair requires an enormous amount of data and expertise. Additionally, promoting fairness requires an approach across the entire data science life cycle and modeling life cycle. All areas must be considered from the approach to data collection to ongoing evaluation of decisions. And, while fairness in AI is not ‘once and done’ or easily solved, the good news is that it is an area of great focus for regulators, academics, and data and analytics industry experts, like our peers at Experian. The growing importance of transparency and explainability Models generally compute calculations that are complex and involve more dimensions than we can directly comprehend. Given this processing step from model-input-to-model-output is unclear, it leads to questions around how a model has come to a decision. Importantly, how can one be sure that the model is behaving as expected? There are different ways to address explainability. One includes an understanding of how different inputs of a model affect its outputs. Shapley values, introduced by Nobel prize winner Lloyd Shapely, consider an aggregate of marginal contributions for all possible combinations. Another technique involves explaining the behavior of a decision by identifying model constants verse variables to extract what drove a decision and how. Yet another method uses counterfactual explanations, identifying the precise boundary where a decision changes. This method is easy to communicate since it involves statements such as if X had not occurred, Y would not have happened. As in the case of fairness, there’s an on-going dialogue around explainability, underpinned by current and yet to emerge new techniques that maintain model accuracy and improve explainability. Artificial intelligence is past its infancy stage. It’s already had an impact on our daily lives and is becoming increasingly ubiquitous. Fairness, along with a transparent and explainable approach are key ingredients to help this field continue its transition to maturity.

In a world drastically and constantly changing, industries and technologies are being transformed at a rate never before experienced. Recently, I had the opportunity to speak with Roy Schulte, Distinguished VP Analyst at Gartner, about trends in the evolving world of big data, advanced analytics, and decision intelligence — trends that are powered by advancements in cloud technology, machine learning, and real-time streaming. Below, I will share a summary of key highlights. The growth of decision management More businesses are implementing decision management. They want to make more automated decisions to accelerate outcomes while improving applicability. In tandem, decisions are becoming more complicated with regulators increasingly expecting decisions to be explainable and with audit trails built-in. Equally, the combination of machine learning and decision management has placed greater focus on the importance of avoiding bias. Bringing all of this together promises continuous decision improvement; updating models and strategies in days or even hours rather than weeks and months. Essential in responding to the rapidly changing environments, none more so than the impact of Covid-19. As businesses are implementing decision management, they are putting the new systems into the cloud. Based on a Gartner survey in mid-2020, 67% of respondents said their digital business platform will be cloud-native application architecture. It’s the primary criteria for the architecture of many of these new systems. Migrating to the cloud The reason decision management is going to the cloud is the same reason other areas of business are taking this step. Organizations are highly motivated not to run their own systems. There is no competitive advantage to doing so. They want to entrust it to others so they can focus on what the business does best. The migration will continue gradually to the cloud, with a current acceleration based on Covid-19. In a recent Gartner survey, 65% of respondents said the pandemic accelerated their plans and funding for doing digital business. Most models and strategies will be built in the cloud, and the actual runtime decisions will be distributed with some on the cloud, some on-premise in a data center, and some out in the edge in a mobile device. Real-time streaming In the past, traditional business information was done on static, snaps of data from the past. Today, much of this is going real-time, depending on the kind of decision that is being made. For example, when a customer is visiting a website, there are mere seconds to generate the next best offer. This is a real-time decision. However, some decisions do not require real-time, such as the strategic decision to acquire another company or not. That is why it’s important to align the decision speed and cadence with the actual business problem. If real-time data will be used, such as for an e-commerce situation, some of it must be streaming data. With the increase in factors taken into account when making decisions, you need data that is connected, contextual and continuous. The data must come for your entire ecosystem, not a single department, but across your company, business partners, your customers, or market data. Streaming examples for e-commerce might include location, what the person has been doing on the web recently (clickstreams), and records of contact with your business such as calls and emails. A real-time lookup is involved with inventory and external factors like credit rating through an API, and customer data will include historical and real-time. With these factors, real-time decisions for e-commerce will be more effective, with higher yield rates and lower fraud rates. Machine learning in model building and execution Machine learning (ML) is making predictions, not decisions. When a prediction is made and a score is provided, a rule must be applied to determine outcomes based on the score. If considering rules or analytics, the truth is that in most cases, both are needed. The goal of ML in decision management is to have applications that are easier to develop, faster to develop, and lead to more accurate outcomes. To achieve this objective, a process covering all stages of a decision cycle is needed — Observe: Getting connected, contextual, continuous intelligence. Governance is key at this step to know where data is coming from and that it will be used in authorized ways. ML models and strategies must avoid bias and alternative data sources and lending criteria should be considered to expand the business without incurring increased risk. Orient: The next step is putting the data in context. When dealing with models at scale, it’s important to be able to track outcomes through tools such as performance dashboards. Eventually, this will lead to the hyper-personalization of models. Decide: Once models are built, strategy, rule authoring, and approvals are needed. Workflow and collaboration mechanisms help manage the process and accelerate the pace of developing new decisions. Act: Next comes the deployment of models. Using logic to make decisions across multiple applications accelerates deployment, often referred to as centralized management or reuse of decision factors. Feedback: Finally, continuous logging of decisions and effects of decisions. Tracking provides the ability to audit past decisions, explain what was done, and accurately post hoc remediate. Ongoing feedback also enables continuous decision improvement at an accelerated pace. The future of decision management includes decision intelligence In summary, there are five considerations for the future of decision management — A systematic approach to decision making, including a lot more automation and decision intelligence, is clearly on its way. Migration to the cloud is well underway with acceleration thanks to Covid-19. Equilibrium will be reached where some decisions are made at run time at other locations, but most of the development of decision-making, modeling, and strategies will be based on cloud platforms. Data science ML vendors have not focused on decisions. Some may come to realize the reason you do analytics is decisions and broaden the scope of what they do, or they may stay focused and instead partner with other vendors to enable end-to-end decision making. For certain kinds of logic, graph databases and graph analytics can be very powerful. Likely this will become a big part of decision intelligence going forward. Finally, there is huge untapped potential in optimization technology to improve decisions either at development time or even at run time by applying optimization techniques. This could lead to achieving the full vision of artificial intelligence. Related stories Insights in Action Podcast: Identifying the core capabilities your business needs to get MLOps right New Tech Talks Daily Podcast: Machine learning and AI in business — investment trends pre- and post-pandemic In digital transformation, small wins lead to big outcomes

Check out the 10 most popular stories of 2020 that will help to kick-start your 2021. It includes a look back on how trends evolved throughout the past year, which trends will be durable in the new year, and what the global pandemic has taught us about creating meaningful relationships between consumers and businesses. Top 10 list of the best stories in 2020: 10. Model recalibration drives impactful results during constant change Banks have managed through stressed scenarios in the past but none have ever had to predict customer behavior in a pandemic. General indicators of risk or stress didn’t reveal enough about what was going on in customer portfolios. Active model calibration in our current situation had a measurable effect on approvals and expected losses but executives still needed to regain control over disrupted models. Read full article 9. Digitally managing your at-risk customers most impacted by Covid-19 Lenders felt a tremendous amount of pressure this year trying to help reduce the impact of the financial burden Covid-19 put on consumers by supporting payment forgiveness and deferment programs. This made it difficult, though, to understand changes in the credit profile of a previous solvent customer and mobilize their operations teams to service these good but at-risk customers. Read full article 8. The rising need for identity verification Consumers turned to digital when mass closures of businesses prevented in-person transactions. Even as some businesses re-opened with precautions in place, many consumers still felt it was safer to do business online emphasizing the importance of security and identity verification. But while some level of friction invokes a sense of security, too much or unnecessary friction had an adverse effect. Read full article 7. Proactively restructuring debt to help improve customer affordability At the beginning of the year, no one could accurately predict how the world would be impacted by Covid-19 or how long it would last. Customer affordability models shifted into unknown territory and businesses tried to figure out how to assess customer risk in this new context. Lenders relied on the customer data and insights available to them and needed them to work harder at anticipating changes. Read full article 6. Be mindful of these 3 strategies when engaging customers digitally The road to digital was already being paved when the pandemic started but consumers and businesses were pushed there to engage en masse this year. There were practical challenges that needed to be addressed in the short-term, like managing call volume with a remote workforce. But more importantly, it put the spotlight on massive areas in need of modernization, such as the management of liquidity and risk. Read full article 5. Banking trends and opportunities in the post-Covid-19 crisis era This year was marked by adaptation, resilience, and reflection – which can be said for our personal lives – but in the context of the banking industry, it created an opportunity to change or accelerate priorities. Moving operations to the cloud, making sure decision strategies are fit-for-purpose, and applying analytics in a more useful way are some of the stickier trends we’ll likely see continue in 2021. Read full article 4. Why consumer trust in the digital experience is so important in a pandemic era Despite the uncertainty of this past year, one thing remained certain – cultivating customer trust is critical to brand loyalty. Digital customer trust, however, required businesses to consider several specific factors that inform and build trust. Digital adoption was mistakenly considered the most important of those yet being treated fairly, customer recognition, and fraud prevention were stronger signals. Read full article 3. Game changers: Women in Artificial Intelligence Artificial intelligence offers a lot of value, especially when used to better support customers’ financial needs. As more businesses processed huge amounts of data with advanced analytics and AI this year, human oversight was key to ensure transparency and explainability. This “human element” was the inspiration for an article mini-series featuring five women who are making a real difference using AI innovation. Read full article 2. Digital transformation through cloud-first decisioning The credit and fraud risk decision management landscape changed this year – including how the customer journey is being redefined. Mounting consumer expectations for a better digital experience meant the front and back end of a business’ operations were no longer mutually exclusive. Cloud-based applications was the reset needed to move away from functional and product silos to focus on the customer. Read full article 1. Covid-19 as a gateway to fraud Fraudsters are opportunistic which exposed another ugly side of the pandemic throughout the year. As people and businesses moved to digital to engage with one another, criminals exposed weaknesses in the tools, processes, and systems used to protect those interactions. Investment in fraud prevention was already on the rise but steadily increased throughout the year as new fraud trends emerged. Read full article