Month: April 2019

Mike leads consumer lending business for a global financial organization. He is looking to upgrade its existing systems from a legacy platform to an AI-based solution. He has heard about the power of AI and how it can help businesses. Within his organization, Mike can see its applicability in risk models and also in taking some pro-active action to check whether the system can predict customer delinquency. As Mike explores this option further, he also understands why existing risk segmentation models are rudimentary based on static rules or statistical models. However, should Mike need to change his core collection and servicing platform to get benefits of technology? He fears that a rip and replace of collection systems would take too long and his business would get impacted if he is not able to use the power of AI sooner. After double-clicking the initiative to implement AI in-house on top of the existing collection system, Mike starts to lose hope in getting faster results. He finds out, that manual data preparation, feature engineering, and model building is a time-consuming process. Also, productionizing and realizing business outcome is a massive challenge with in-house AI implementation. However, Mike also has other problems to tackle. In spite of technology coming on leaps and bounds over the past decade, AI remains a black box, and this presents a significant challenge for audit and regulatory compliance. Although Mike can choose to be guided by an AI system, if he cannot defend or explain the recommendations it makes, and this would place him in a vulnerable position in front of auditors and model risk validators. How can he balance what the business needs faster with what regulation and compliance demand? If you identify with Mike’s conundrum, read on.

Fast becoming the cornerstone of digital transformation, the proliferation of AI has evolved from automating repeatable tasks to powering human-led decision making. Its significant advantages, however, are weighed down by some very real challenges – compliance, traceability, and auditability. This absence of transparency does little to inspire trust in AI-powered decisions, especially when the human being taking the final call accepts liability for any errors. In the financial industry, the need for organizations to make regular judgments about customers exacerbates the problem. Companies need to be able to explain and back the decisions they make, while customers need to understand the systems assessing them, neither of which is possible with conventional cognitive applications. In the financial sector, from fraud detection to lending, we have seen that the hurdle of transparency offsets the appetite for AI adoption.

The alternative, however, is hardly ideal. Consider the lending sector; Data models are updated no more than twice each year, generating dated results may not correspond with the actions eventually taken on consumer accounts. They also take into account a limited number of criteria before creating a broad and inaccurate borrower risk profile. This traditional output from legacy systems offers sub-optimal segmentation, preventing businesses from understanding the risk profile of their delinquent portfolio and, more importantly, the true recoverability of overdue accounts. Intelligent systems could transform these operations by introducing efficiency and accuracy that saves millions of dollars while increasing customer satisfaction. The answer lies in a solution that offers the transformative power of AI while providing human-level contextual explainability.

That’s why we built CollectEdge. A thin layer of AI-powered intelligence that sits on existing core platforms, CollectEdge is a powerful solution that integrates smartness, transparency, and accountability into existing collection systems. CollectEdge helps organizations reduce delinquency rates, improve recoveries, and be more operationally efficient through actionable business insights backed by clear explanations. The solution offers a substantial advantage over existing systems, and its core advantage moves well beyond efficiency. Here’s why.

When you make a machine learning model, the objective is to ignore correlated information and create a feature set that is a combination of two to three data points. We understood that it was essential to develop a system that could generate smarter data models, interpret their output, and offer an easily accessible narrative. That’s why with CollectEdge we built a model that tells you the decision and then shows you which influencing factors helped arrive at the result. These insights are not available in a conventional analytical or augmented analytical system, but are offered by CollectEdge because of its ability to provide decision-level explainability. More importantly, it gives businesses a thorough and explainable understanding of the influencing factors, the reason behind their selection, and their impact on the final recommendation. Companies can also identify and utilize complex links between multiple influencing factors to inform upstream processes, future models, and collection strategies.

Every prediction that CollectEdge makes is derived using specific influencing factors. It’s important to note that these are not just analytics-driven insights. By using a wide variety of influencing factors including, for instance, macroeconomic indicators, CollectEdge offers end users powerful predictive intelligence based on a comprehensive view of the data subject. Combined with the solution’s explainable AI, this ability means that users can now see the thinking and data points behind each recommendation allowing for better auditability, compliance, and regulatory discipline. Additionally, if there is data available outside of the current base product model, it can easily be added using CollectEdge without having to repeat the model creation process.

We believe this eliminates the need for organizations to choose between a less elaborate method with a high level of explainability and a sophisticated tool with little or no transparency. To make it easier for financial institutions to adopt AI, we integrated explainability into CollectEdge in a way that feels intuitive and actionable to end users. While AI-based systems are usually a tradeoff between accuracy and clarity, CollectEdge’s model-agnostic approach provides enhanced explanations. These are provided at the local level instead of model-level recommendations, allowing teams to extract actionable business insights that can be fed upstream into the process and, in turn, deliver real downstream impact.

The best part of the solution is that it can deliver sizable improvement in business performance, including reduced charge-offs, savings on call investments, improved CSAT scores, and near real-time models. To allay any implementation concerns about the magnitude of effort or investment, CollectEdge sits unobtrusively on top of existing systems without any need for ‘rip and replace’ allowing organizations to experience the power of AI adoption in production.

With CollectEdge, companies can now adopt a future-proof AI-led decision-making process that their leadership, regulators, and customers can trust. This addition of efficiency, intelligence, and clarity frees up resources and investments that could drive significant business growth. Now that needs no explanation.

To experience the power of CollectEdge, schedule a demo today.

“I wish the collectors would understand me better. Being a customer for over 15 years should mean something. Money is tight just now but it’s not like I’ll never pay them back. I wish they could respect my schedule and send me an email instead of the incessant calls when I’m at work. Is that too much to ask?”

“I wish the business understood that my primary responsibility is to collect on the portfolio. There are roll rates and charge offs to manage. Yes, customer experience is important, of course, it is! But, I manage a portfolio with millions of customers and don’t have the tools to personalize my approach. My team needs help.”

If this sounds familiar, you’re not alone. Creating a great customer experience in lender-debtor engagements, especially delinquency, can be difficult. On the one hand, the traditional approach to collection results in inefficient outreach since it doesn’t take into consideration the customer’s channel preference. On the other, this lack of focus means the costs of staffing and account management are through the roof. For a customer, the absence of nuance in the debt recovery process can be distressing. In the case of collections, specifically delinquency, the focus on risk over customer experience can also cost companies valuable relationships.

In order to address the problem, we first had to develop an intrinsic understanding of the context. Financial institutions have to balance a number of often conflicting priorities such as capitalizing on credit appetite, mitigating risk, and minimizing bad debt all while ensuring a quality customer experience. Legacy systems continue to inform collection strategy, applying a one-size-fits-all approach devoid of insights into an individual customer’s often unique payment situation. Also, risk segmentation remains rudimentary, based on a myopic view of the debtor generated by a limited selection of variables such as payment history, DTI, and credit scores. As a result, every year, sub-optimally prioritized collection efforts informed by a limited view of the borrower cause companies to spend hundreds of thousands of dollars on call investments covering even low-risk profiles. These well-intentioned but misdirected efforts can cause significant customer distress, damage the reputation of the business, and, in some cases, result in lawsuits.

That’s why we built CollectEdge – a thin layer of AI-powered intelligence designed to help banks and other lending institutions make faster, smarter, and more effective business decisions. CollectEdge is an intelligent bolt-on that sits on top of existing collection systems, instantly transforming their performance by adding efficiency, perspective, and explainability. Currently, the time-intensive nature of manual model creation means that they are only updated once or twice a year, resulting in dated information that may not be reflective of a borrower’s circumstances. Up to 40% faster than traditional manual segmentation efforts, CollectEdge enables organizations to frequently create risk profiles in line with a customer’s actual financial ability and propensity to pay. Companies can now automate the effort of data cleaning, feature engineering, and model creation, saving millions of dollars in resource and process time. If you think those efficiency gains are substantial, the application’s intelligence is truly transformative.

The intelligent insights from CollectEdge are derived from a holistic customer understanding generated by a wide variety of data sources – credit history, customer behavior across channels, both internal and external, and macroeconomic factors like GDP and employment rates. The product is able to deeply analyze a wide variety of structured and unstructured data including customer calls, emails, texts, public posts, blogs, tweets, and comments, enabling it to segment delinquent portfolios by relative risk based on intelligence at an individual level. Furthermore, this mechanism allows the segmentation to be based on a unified view of the customer across products and channels, as opposed to a product-specific view of the customer that never offers the whole picture. In addition to unearthing new influencing factors through this comprehensive approach, the product also discovers complex linkages between these factors, empowering companies to extract actionable business insights for process improvement and customer excellence.

CollectEdge then offers account-level treatment plans and collection strategies with each recommendation substantiated by a list of influencing factors, augmenting business processes with human-level intelligence at scale. With a deep view of the customer and their preferences including the frequency of contact, mode of outreach, time to reach out, and the tone of communication, the outreach team on the floor can now personalize collection strategies with unprecedented specificity. The result – genuine business impact. CollectEdge’s implementation model includes a commitment to demonstrate business results such as a 25-50 basis point improvement in reducing charge-offs, better CSAT scores, and even a 10% increase in call savings.

While the industry has started to explore best-in-class technology products and AI-enabled tools, most of these still remain in the PoC phase. And since core systems are procured and implemented at significant capital investment, a rip-and-replace approach isn’t just expensive, but also fraught with risk given the absence of demonstrable results in production. The ease of implementation and configuration means CollectEdge helps businesses develop a competitive advantage by shifting their thinking from a reduction of cost and effort to an increase in better decision making and customer satisfaction. The application’s vertical-specific offerings easily feed intelligence to existing systems generating updated segmentation for users within weeks of implementation. This makes CollectEdge a sizable opportunity for financial institutions to test the impact of AI in production before committing to larger digital transformation efforts.

In a highly competitive landscape where each basis point counts, the seismic shift from reactive damage control to predictive analytics and segmentation allows organizations to balance retention and customer experience efforts with risk management. Click here to discover how CollectEdge can help you create a better experience for your collections teams and customers.

References:

https://www.nclc.org/images/pdf/pr-reports/report-analysis-debt-coll-ftc-data.pdf

https://libertystreeteconomics.newyorkfed.org/2019/02/just-released-auto-loans-in-high-gear.html

https://www.kansascityfed.org/en/publications/research/mb/articles/2018/auto-loan-delinquency-rates-rising

https://www.cnbc.com/2019/03/16/robocalls-about-your-bills-can-pour-in-every-day-all-day.html

https://www.experian.com/blogs/ask-experian/what-do-americans-hate-most-about-debt-collectors/

https://www.consumerreports.org/consumerist/new-rules-would-require-debt-collectors-have-proof-you-actually-owe-money/

https://www.pymnts.com/news/artificial-intelligence/2018/ai-debt-collection-consumer-behavior-brighterion/

https://www.insidearm.com/news/00043910-artificial-intelligence-can-it-help-colle/

https://www.mirror.co.uk/money/debt-collection-firms-creating-more-13951289

https://www.nclc.org/issues/consumer-debt-collection-facts.html

Elevating levels of debt magnify the chances of delinquency, which can considerably reduce profit margins and surge costs for lenders.

Furthermore, since lofty debt levels render the economy to extreme vulnerability, inability to check this stalemate in time can also give way to a global economic meltdown.

Thus, with the amount of global debt accumulating every year, traditional processes have been able to do very little to arrest the problem, creating the need for advanced systems that can inhibit galloping delinquency rates by also enhancing customer experience.

PROBLEMS IN TRADITIONAL COLLECTION PROCESS

In 2017, the global debt reached an all-time high of $184 trillion in nominal terms, the equivalent of 225% of GDP. On average, the world’s debt exceeded $86,000 in per capita terms, which is more than 2½ times the average income per-capita.¹

To recover loans, traditional systems deploy time-intensive manual correspondences that impede proper resource utilization. Also, they only analyze recent internal transaction data and lack accurate risk segmentation models. Regular updates of data since collection or loan approval are also not available with these traditional systems.

The use of AI and machine learning (ML) has become obligatory for businesses to beef up collections and skirt the initial roadblocks presented by legacy systems.

AI IMPROVING PERFORMANCE RECORDS

When it comes to collection strategy, AI-powered machines emulate cognitive human behavior to solve a host of issues discussed below:

Powered by advanced ML capabilities, CollectEdge is a data-driven intelligent application designed to help lending and collecting organizations reduce delinquency rates and boost recoveries. CollectEdge can assist companies that want to make existing debt collection systems more intelligent and seek a balance between mitigating loan losses and enhancing customer experience.

COLLECTEDGE

CollectEdge studies data across channels and looks up statutory/regional regulations to recommend appropriate channels and time for contact. It understands personality traits, negotiates and connects with borrowers based on e-mails, texts, public posts, blogs, tweets and comments. In addition, it offers preventive insights, risk predictions and ML-based queue prioritizations that focus on profiles with high chances of recovery and personalized repayment plans for delinquent customers.

Apart from major perks like lower delinquency rates, reduced charge-offs and higher operational efficiencies, the software offers a unified view of customer accounts and can be synced with existing core collection systems. With CollectEdge, companies are not required to rip and replace their existing legacy systems and train their talent resources on new technology. Easy implementation and faster time-to-market, unbiased data model and easy access for audit teams are factors that can make the product popular among companies concerned about value cycles and understandability among auditors.

Sources:

New Data on Global Debt, https://blogs.imf.org/2019/01/02/new-data-on-global-debt/, January 2, 2019

I vaguely remember reading about classification as living and non-living things in my childhood days, but now with IOT, we can visualize redefining them, and comfortably say, I can have a chair that communicates, shoes that connect to a network, door lock that can sense me walking close by. This technology enables the things around you to sense, communicate, network and produce new information, thereby adding life to things. It marks a crucial turning point for future in the same way as invention of computers and Internet did a few years ago.

IOT is still yet to get the limelight it deserves. Reasons could be many, and this raises the inquisitiveness to delve deeper into what goes into creating these thinking and talking things. There are number of questions like how can things talk, how do they connect to network, how are they controlled and how secure are they? IOT as I understand, extends connectivity to everyday objects. Going a little deeper, it also helps in monitoring, analyzing and controlling these devices.

IOT Ecosystem: Smart device in Smart space

To summarize the flow – the sensors generate data, controller collects the data, sends to the cloud through Gateway. The data is analyzed over cloud; the analyzed output is sent back to another controller which further triggers the device to perform an action. What makes this process intelligent is that these smart devices can now take part in your business processes and they use intelligent interfaces to connect with the rest of the world.

IOT Security: An extra eye and an extra ear right in your home

With almost everything on the Internet, the risk of personal information getting leaked is high. The threat may not be only to invade public spaces but can also influence decision making. While the policy makers, encryptions and firewalls would do their part to secure IOT, we also need to own this piece. There needs to be a limit on whether we really need everything on the Internet or just a few things.

IOT and Banking

Banking is just entering into the space of IOT. Currently there are use cases like tracking ATM usage, tracking crop yield, tracking vehicles sold on loan and this gives a lot of data to the banks. Once banks convert the IOT data into useful information, they can engage with their customers better and increase their market share. The core component in banking is money which is already being used digitally on mobile, online and other channels. However, we are currently using indirect applications of IOT. It can be of more significance if used in branches where the customers’ inflow and outflow can be tracked. IOT can also prove to be a major differentiator to improve branch staff and field staff services where sensors can be used to track the nature, extent, and quality of services provided.

Exciting times ahead

It might take a while before the presence of IOT is felt in our daily lives but thanks to the underlying technologies like advanced wireless networking, cloud computing, standardized communication protocols, smaller size silicon chips and finally falling prices of computing resources, it won’t be long before we start seeing various corporate IOT applications deployed in our surroundings.

Mass customization in application design is the concept and act of designing interfaces that make every user believe that a particular interface is designed exclusively for them. Personalization at scale is the key driver for mass customization. Any task or activity, persona regardless, spans various stages such as interaction, operation and business process. In most of the cases, it cuts across multiple application boundaries as well. Personalization should span all these dimensions of interaction, operation and business. Context plays the biggest role in determining personalized behavior.

Key elements required for context include:

Please note, the above list is not exhaustive.

All dimensions of context are essential for an interface designed for personalization. In this article, we look at one of the dimensions – “where” – and explore it to figure out if there exists a pattern.

Access phase which followed automation phase in the banking industry focused on enabling the customers to carry out banking transactions using digital interfaces like ATM, online banking and mobile banking. All the controls and personalization were designed around these consumption points or touch points which are “Technical” in nature like ATM, browser, mobile, kiosk etc.  For example, banks can setup different limits (which can be personalized) for browser, mobile and ATM.

During the next wave, banking services were made accessible through social media applications like Twitter and Facebook. Now, Facebook can be accessed using a browser on a desktop or through a mobile application. Hence “Application” provides context, with design controls for suitable behavior to drive personalization. In this wave, “Technical context” was immaterial. Hence the controls and personalization are around “application context” irrespective of their access points like browser or mobile.

During the next wave banking services were made accessible through third-party application through open banking APIs. This is a very interesting aspect since the provider of the banking services is neither aware of the technical context (can be browser or mobile) nor aware of the application in which banking service would be consumed. Here the context would be “Third party”.

This changing context is captured with examples in this table:

Channel	Supported By	Application	Technicality	Context to be considered
Online Banking	Bank’s own channel	Online Banking	Browser	Technical
Mobile banking	Bank’s own channel	Mobile Banking	Mobile	Technical
ATM	Bank’s own channel	ATM banking	ATM	Technical
Facebook	Facebook	Facebook	Managed by 3rd party	Application
Twitter	Twitter	Twitter	Managed by 3rd party	Application
AISP	Third party	Managed by 3rd party	Managed by 3rd party	Third Party
PISP	Third party	Managed by 3rd party	Managed by 3rd party	Third Party

Banks should recognize the various dimensions to provide personalized experiences amid changing context, and make sure that the right applications, advice, and assistance are made available. Designing such as experience cuts across interaction, operation and business layers.

In these increasingly digital times, interactions will originate on different channels and will also be influenced on these channels. And many of these channels will not be owned by banks. Context at all times, irrespective of the channel, will be the cornerstone of personalization in this digital future.

Customer delight is the key to success in business, and cloud is one of the foundational building blocks and enablers for the digital technologies like analytics, machine learning and artificial intelligence that can serve to enhance customer experience significantly. Instead of spending time on concerns about infrastructure and resources, enterprises can shift focus on their core business by using the public cloud. As per the sixth Cloud Native Computing Foundation survey, the top three benefits of cloud native technology include faster deployment time, improved scalability and cloud portability.

The public cloud is defined as computing services offered by third-party providers over the public Internet, making them available to anyone who wants to use or purchase them. They may be free of charge or sold on demand, allowing customers to only pay as per usage for the CPU cycles, storage or bandwidth they consume. Multi-clouds or multiple public clouds are also being used by banks to avoid vendor lock-in and reap the benefits of competition among cloud providers.

The main benefits of using a public cloud for banks are:

Security concerns with respect to adoption of public cloud is misplaced. With inbuilt identity and access management and other design considerations for securing applications and secure DevOps processes as part of public cloud infrastructure, bank users and customers don’t need to worry where the application is deployed regardless of location. An end user advocacy group body called Cloud Customer Standards Council has come up with a reference architecture for securing workloads on public cloud which can standardize security on cloud.

Banks need to keep the following aspects in mind while assessing readiness towards Public Cloud:

Banking Visionaries’ Council in a paper on Banking on Public Cloud – Reimagining Business Agility, lists the key cloud migration strategies and Cloud Governance and operating model that can be adopted by banks. Cloud migration strategies that can be adopted by banks include Rehost, Replatform, Repurchase, Refactor, Retain, Retire where “Rehost” can be used when change required is minimum and “Replatform” or “Rearchitect” when change required is significant. According to the paper, a bank must design its cloud architecture to be flexible enough to support both its infrastructure and banking applications at different stages of evolution, with the bank using public cloud and SaaS for its IT needs in its final stage of cloud journey.

The journey towards cloud and cloud native technologies makes a real difference to the profitability of a bank. Keeping an open mind towards these technologies ensures business success.

References: https://medium.com/aws-enterprise-collection/6-strategies-for-migrating-applications-to-the-cloud-eb4e85c412b4

We all receive recommendations on online portals or apps for movies, books, music, videos, food etc. based on our history of online transactions. Similarly, for transactions performed over the counter we receive recommendations on products, information about discounts and offers on our preferred brands, based on the history of transactions performed using a privileged membership card. All these insights empower us to make purchase decisions.

Organizations are doing everything possible to offer products that customers want to buy based on insights about their lifestyle using advanced analytics on transaction history data. This allows them to improve the ROI on their promotions by eliminating irrelevant offers.

Similarly, banks have a wealth of data at their disposal from multiple touch points combined with customer interactions on social platforms to arrive at recommendations. With the help of all the data they have, banks can understand the latent need of their existing customers, and contextualize their recommendations, offer pre-approved personalized financial products through their digital channels and encourage customers to buy them, thus considerably increasing the possibility of success. The cost for the bank this way will be a fraction of what it can offer through any other mode, thereby increasing the profitability and competitiveness.

A data-driven approach to technology allows banks to improve business outcomes and innovation. Adoption of analytics solutions provides an opportunity for banks to:

It is essential that analytics capabilities be accessible to all functions within the organization to empower employees to take decisions based on actionable insights.

Offering the customer convenience, control and choice using data analytics does not only make the customer happy but rewards the business with repeat purchases and hence customer retention. Data security also needs to be factored in to avoid loss of trust of stakeholders involved. Thus a robust framework of security guidelines and protocols need to be in place in this journey towards true customer delight.

The title of the blogpost may lead you to think that I am going to compare private enterprises vs. public enterprises. Or from a digital standpoint: private network vs. social network. From a proprietary standpoint: private and confidential information vs. open source content. From a data standpoint: private cloud vs. public cloud. From a personal identity standpoint: privacy vs. publicity!

We should not ask the age of a female and salary of a male was the saying in the past. But things are changing in the digital age where both are at par. And moreover, few things are really private now.

It is essential to be part of a network to share information, to learn from others experiences, to stay connected and be up to date about what is happening around. Think for a while: Are the people who were initially a part of your network still required to be part of the network? Especially when it comes to a private network with high turnaround and people moving between projects, roles and careers, is the “private” information being shared still private?

We love to share our feelings, check-in details, pictures on social network as some people say there is nothing to hide or that is what social media is meant for. In fact, the more social we become the more we will be targeted with better product and service offerings by the digital giants who are always listening. But for choice and convenience are we giving up control over our information. The answer lies in having a control over our personal data – what we consent to, who we give our consent to and what information we want to share.

In certain cases, the data is used for research purposes, to come up with a point of view and arrive at trends so that users of this information can make a better decision. All good intentions! But in most cases the information is used for monetary benefits by taking advantage and misusing genuine customer consent.

Privacy is your right to control your personal information such that you can decide how much others should know about your personal information. It is something very fundamental to trust. Private and sensitive information may act as a competitive advantage for companies to differentiate themselves. But for individuals, information such as their health condition, for example, they may not want to reveal to anyone other than their doctors and near and dear ones. Being open about your condition with your doctor is good to get the required assistance but that information seldom needs to change hands any further. Even this data is being bought and sold openly in this digital age to generate revenues because of its sheer value which is an unintended consequence of further information exchange.

Privacy and data breach is considered to be a standard business practice to offer better service to customers at lower costs and not something which should needs to be prevented. Overlaid with digital technologies like cloud, mobility, analytics, social, we continue to aid the proliferation of more and more information.

Protection of privacy lies with each entity. Business entities need to understand whose data they are trying to monetize and what the right way to use is. Individuals need to be careful and choose what to share, what not to share and have control over their personal assets i.e. information in this case!