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How predictive modelling and optimization can maximize recovered amounts with a focus on Next Best Action assignment.
New IDC MarketScape: Worldwide Enterprise Fraud Solutions 2024 Vendor assessment provides valuable resource as organizations face increased fraud.
With the potential annual value of AI and analytics for global banking estimated to reach $1 trillion,1 financial institutions are seeking out efficient ways to implement insights-driven lending. As regulators continue to supervise risk management, lenders must balance the opportunity presented by AI to determine risk more accurately while growing approval rates and reducing the cost of acquisition, with the ability to explain decisions. The challenge of using AI in building credit risk models In a recent study conducted by Forrester Consulting on behalf of Experian, the top pain points for technology decision makers in financial services were reported to be automation and availability of data.2 The implementation of accessible AI solutions in credit risk management allows businesses to improve efficiency and time-to-market metrics by widening data sources, improving automation and decreasing risk. But the implementation of AI and machine learning in credit risk models can pose other challenges. The study also found that 31% of respondents felt that their organization could not clearly explain the reasoning behind credit decisions to customers.2 Although AI has been proven to improve the accuracy of predictive credit risk models, these advancements mean that many organizations need support in understanding and explaining the outcomes of AI-powered decisions to fulfil regulatory obligations, such as the Equal Credit Opportunity Act (ECOA). Moving from traditional model development methodologies to Machine Learning (ML) As lenders move away from traditional parametric models like logistic regression, to ML models like neural nets or tree-based ensemble methods, explainability becomes more complex. Logistic regression has for many years allowed for a clear understanding of the linear relationships between model attributes and the outcome (approval or decline). Once the model is estimated, it is completely explainable. However, ML models are non-parametric, so there are no underlying assumptions made around the distribution (shape) of the sample. Furthermore, the relationships between attributes and outcomes are not assumed to be linear – they’re often non-linear and complex, involving interactions. Such models are perceived to be black boxes where data is consumed as an input, processed and a decision is made without any visibility around the inner dynamics of the model. At the same time, it is possible for ML models to perform better when accurately classifying good customers and those deemed delinquent. Ensuring transparency and explainability is crucial – lenders must be able to identify and explain the most dominant attributes that contribute towards a decision to lend or not. They must also provide ‘reason codes’ at the customer level so any declined applicants can fully understand the main cause and have a path to remediation. The importance of developing transparent and explainable models By prioritizing the development of transparent and interpretable models, financial institutions can also better foster equitable lending practices. However, fair credit decisioning goes beyond the regulatory and ethical obligations - it also makes business sense. Unfair lending leads to higher default rates if creditworthiness is not accurately assessed, therefore increasing bad debts. Removing demographics considered to be the ‘unscored’ or ‘underserved’ (those who are credit worthy but do not have a traditional data trail, but instead a digital footprint comprised of alternative data) can also limit portfolio opportunity for businesses. For these reasons, it is critical to remove or minimize model bias. Bias is an upstream issue that starts at the data collection stage and model algorithm selections. Models developed using logistic regression or machine learning algorithms can be made fairer through carefully selecting attributes relevant to credit decisioning and avoiding sensitive attributes like race, gender, or ethnicity. Wherever sensitive metrics are used, they should be down-weighted to suppress their impact on lending decisions. Some other techniques to mitigate bias include: Thoroughly reviewing the data samples used in modelling. Fair Model Training - Train models using fairness-aware techniques. This may involve adjusting the training process to penalise any discrimination that creeps in. According to Forrester, an essential component of a decisioning platform is one that can “harness the power of AI while enhancing and governing it with well-proven and trusted human business expertise. The best automated decisions come from a combination of both.”3 Developing explainable models goes some way towards reducing bias, but making the decisions explainable to regulatory bodies is a separate issue, and in the digital age of AI, can require deep domain expertise to fulfil. While AI-powered decisioning can help businesses make smarter decisions, they also need the ability to confidently explain their lending practices to stay compliant. With the help of an expert partner, organizations can gain an understanding of what contributed most to a decision and receive detailed and transparent documentation for use with regulators. This ensures lenders can safely grow approval rates, be more inclusive, and better serve their customers. “The solution isn’t simply finding better ways to convey how a system works; rather, it’s about creating tools and processes that can help even the deep expert understand the outcome and then explain it to others.”McKinsey: why businesses need explainable ai and how to deliver it Experian’s Ascend Intelligence ServicesTM Acquire is a custom credit risk model development service that can better quantify risk, score more applicants, increase automation, and drive more profitable decisions. Find out more Confidently explain lending practices:Detailed, rigorous, and transparent documentation that has been proven to meet the strictest regulatory standards. Breaking Machine Learning (ML) out of the black box:Understand what contributed most to a decision and generate adverse action codes directly from the model through our patent-pending ML explainability.References: "The executive's AI playbook," McKinsey.com. (See "Banking," under "Value & Assess.") In a study conducted by Forrester Consulting on behalf of Experian, we surveyed 660 and interviewed 60 decision makers for technology purchases that support the credit lifecycle at their financial services organisation. The study included businesses across North America, UK and Ireland, and Brazil. 2023_05_Forrester_AI-Decisioning-Platforms-Wave.pdf https://www.mckinsey.com/capabilities/quantumblack/our-insights/why-businesses-need-explainable-ai-and-how-to-deliver-it Contributors:Masood Akhtar, Global Product Marketing Manager
As the lines between authentic and synthetic blur more than ever before, we explore four fraud trends likely to be influenced the most by GEN AI technology in 2024 and what businesses can do to prevent them.
Lenders are using automation across the credit lifecycle and intend to invest further in the next 12 months. We look at the use cases for automation and address the key challenges lenders face when automating decisions.
Authorised Push Payment fraud is growing, and as regulators begin to take action around the world to try to tackle it, we look at what financial institutions need to focus on now.
We take a look at five transformative use cases in lending, and organisational priorities for integrating Gen AI into customer lifecycle processes.
Liminal, a leading market intelligence firm recently recognized Experian as a market leader for compliance and fraud prevention capabilities and execution in its Liminal Link Index on Account Opening in Financial Services.
As economic uncertainty continues to loom, the threat of fraud continues to grow and is becoming more sophisticated. It’s only going to get worse. Due to intensifying inflationary pressures, prices and costs have been increasing which has led to financial hardship impacting individuals and businesses. This provides an opportunity and motive for bad actors to figure out new ways to commit fraud. Federal Trade Commission data shows that consumers reported losing nearly $8.8 billion to fraud in 2022, an increase of more than 30 percent over the previous year. PwC’s Global Economic Crime and Fraud Survey 2022 shows 51% of surveyed organisations say they experienced fraud in the past two years, the highest level in their 20 years of research. Additional investments in fraud prevention technology are a priority for businesses to combat these evolving threats, according to Experian's Sept. 2022 Global Insights report, which states that 94% of businesses report it as the top priority. Since fraud is becoming more sophisticated, part of the challenge that businesses face is to constantly evaluate multiple solutions so that they can continuously improve their fraud detection and prevention capabilities. Investments that can deliver the highest ROI are the solutions that are integrated and orchestrated in a comprehensive fraud reduction intelligence platform. This gives businesses the flexibility to manage evolving strategies and mitigate threats with real-time decisioning. Experian’s CrossCore is an integrated digital identity and fraud risk platform. It offers global solutions to help protect businesses from fraud and maintain compliance with regulatory requirements, using real-time risk analytics and decision-making strategies. The platform aggregates various fraud and identity verification sources to consolidate risk and trust decisions for Experian clients throughout the consumer journey. Experian’s CrossCore has been recognized as an Overall Leader, Innovation Leader, Product Leader, and Market Leader in KuppingerCole’s Fraud Reduction Intelligence Platform Leadership Compass 2023. This recognition highlights Experian's comprehensive approach to combating fraud. It validates that CrossCore offers best-in-class capabilities by augmenting Experian’s industry-leading identity and fraud offerings with a highly curated ecosystem of partners which enables further optionality for our clients based on their specific needs. Read the report CrossCore's Capabilities
A changing economic landscape needs a new approach. The new digital consumer is here to stay and they expect businesses to support them with the products and services they need to navigate the rising cost of living, in a secure digital world personalized to them.
Latest Global Insights Report: How supporting consumers in a time of uncertainty can help businesses adapt and grow A changing economic landscape needs a new approach The new digital consumer is here to stay and they expect businesses to support them with the products and services they need to navigate the rising cost of living, in a secure digital world personalised to them. Find out how: Our latest research reveals how economic uncertainty is evolving the experiences and expectations of digital consumers. From increasing the demand for credit options and financial inclusion, to deepening the need for trust, security and being seen. Read the report to find out how businesses can benefit from responding to changing consumer needs - including the additional tools and resources consumers and businesses may need to maintain financial health. What do digital consumers want? The global economy is under pressure with inflation raising prices across the world. In response, consumer behaviour is shifting, as people tackle the increased cost of living, and the prospect of an economic downturn. Digital consumers are continuing to manage their lives online and are expecting businesses to take the lead on improving the digital environment. A quality online experience is paramount, or consumers will move on. 1 in 4 businesses lost more than 10% of their customers in 2021, due to “suboptimal” digital experiences. A range of payment options including BNPL As prices rise, consumers are expecting to spend more online and are looking for varied credit options to help manage their finances. The demand for buy-now-pay-later (BNPL) options is also growing, with more consumers using BNPL to buy household staples. Consumers look favorably on companies that offer BNPL, but companies will have to find the right balance between supporting customers and managing credit risk. 32% of BNPL purchases were for groceries, up from 27% in March. Financial inclusion Economic uncertainty is accelerating the need for greater financial inclusion. Businesses need to find more creditworthy consumers and support them with responsible and sustainable products and services. 1 in 3 businesses is in the process of rolling out financial inclusion initiatives Security and trust As consumer need increases, so does fraud, including cost of living scams. Security is now a top priority for consumers around the world, alongside privacy, convenience and personalisation. 50% of consumers say they’re concerned about their online transactions. However, trust in emerging customer recognition tools is increasing, with consumers’ top three including physical biometrics, PIN codes and behavioural biometrics. Personalisation Consumers who trust businesses are more willing to share their data, enabling companies to create more personalised experiences, which in turn, improves consumer trust. 46% of consumers say that personalisation (receiving offers that fit their needs) is the most important aspect of their online experience. Read our report to discover the challenges and opportunities facing consumers and businesses and the tools, resources and strategies that can help your company get ahead. The survey results represent 6,000 consumers and 2,000 businesses across 20 countries, including Australia, Brazil, Chile, China, Columbia, Denmark, Germany, India, Indonesia, Ireland, Italy, Malaysia, Netherlands, Norway, Peru, Singapore, South Africa, Spain, UK, and US. Download Report
Our recent research revealed that fraud has been of high concern for consumers over the past year, so how can businesses build consumer trust.
