From better predictions to strategic actions: AI and credit risk management examines the current environment and tools to manage it. Financial institutions are facing increased competition, heightened user experience expectations and changing economic conditions. FIs must keep an eye on credit risk once a line is opened or the loan is in repayment. This report offers research and solutions.
- How AI prevents credit losses while reducing false positives
- The scope, objectivity, scale and speed of AI adoption
- AI’s facility for adaptive learning
- AI’s predictive capabilities
- Top priorities for risk professionals
The unlocking AI playbook: credit risk and payments edition examines areas where financial institutions will potentially use (or are currently using) artificial intelligence. This study is based on a survey of 150 U.S. bank and credit union executives representing institutions with assets ranging from $1 billion to more than $100 billion. The unlocking AI playbook gives a clear picture of how FIs are using technology in their current operations and future plans.
- Learn how FIs are using artificial intelligence (AI) to manage payment services and underwrite credit risk
- Understand the various trends and impacts on payments, credit risk and customer lifecycle management
- Discover how financial institutions are using AI to enhance current functions and bring new capabilities
- Learn the adoption rates of various FIs, including where they are deriving the greatest value
The unlocking AI playbook: healthcare edition gives a clear picture of how healthcare payers are using artificial intelligence to prevent fraud, waste and abuse (FWA). In interviews with 47 leading U.S. healthcare executives, PYMNTS analysts examined how artificial intelligence and other systems are used to manage healthcare payers’ businesses, and dives into accounts payable, accounts receivable, and fraud detection and analysis.
- Learn the six most common technologies used by healthcare organizations including artificial intelligence (AI)
- Understand the various trends and impacts of machine learning and artificial intelligence
- Discover the positive impacts that healthcare executives believe would result from using artificial intelligence to optimize their FWA and accounting programs
Healthcare payers have been held back by rules-based, inefficient technology that still allows $240 billion in annual fraud, waste and abuse. Industry analyst firm Aite Group compares Mastercard’s revolutionary use of Brighterion AI in other payment fraud and finds the same platform could transform the FWA space.
Read this report to learn how:
- Healthcare payers can benefit from Mastercard’s experience in payments fraud detection, coupled with data scientists and FWA subject-matter experts
- Fraud can be significantly reduced through the combination of AI tools, using unsupervised learning to update schemes in real time while using supervised learning to reveal patterns and anomalies
- Payers can reduce claims processing and approval time to catch healthcare fraud before they pay providers
The unlocking AI playbook: FI edition gives a clear picture of how banks are using artificial intelligence in their current operations and future plans. In interviews with 150 leading U.S. bankers, PYMNTS analysts examined how artificial intelligence and other systems are used to manage financial institutions’ businesses, and dives into payments, compliance, credit risk management and fraud protection. This report updates the 2018 findings of this biannual survey.
- Learn the six most common technologies used in financial institutions, including artificial intelligence (AI)
- Understand the various trends and impacts of machine learning, AI and Smart Agents
- Discover the positive impacts that FI executives believe would result from using Smart Agents to optimize their fraud and credit risk programs
- Evaluate developments in technology use between 2018 and 2020
Most financial institutions use data mining (92.5%) or business rules management systems (65%) to detect and prevent fraud, but success rates are only 28 percent and 18 percent. We asked them, “Why?” The answers were surprising.
- Learn the most common technologies used to predict and stop fraud, and their effectiveness ratings
- Understand the various impacts and benefits of machine learning, AI and Smart Agents
- Discover the positive impacts that FI executives believe would result from using Smart Agents to optimize their fraud programs
- Determine the return on investment of implementing AI with Smart Agents to prevent, detect and stop fraud in your organization
The Aite Group, the leading analysts of financial services technologies, named Brighterion the most scalable AI platform used for anti-money laundering (AML). Aite found our platform enables 62,000 transactions per second, more than 2x that of our nearest competitor. Our customers also said we’re leaders in client service.
- The case for ML in external and internal fraud detection, compliance and AML
- Key trends within the ML platform and how technology is evolving to address your challenges
- Key statistics and projected spending
- Interview results from more than 40 fraud and AML executives on five continents
In our ongoing research series on AI for banking, our panel of more than 200 financial executives tell us their experiences with the advantages and limitations of AI, the benefits of adopting Smart Agents to overcome those limitations.
- Learn the limitations and advantages of using AI and ML in financial institutions (FI), including staffing concerns
- Understand the functionality of six of the most common AI and ML tools, including how satisfied FIs are with them
- Discover the positive impacts that FI executives believe would result from using Smart Agents to optimize their AI program
- See where your organization fits into the landscape of Smart Agent adoption—and where this technology is most beneficial
Technological innovation has produced a whole new type of function within financial institutions (FIs), one where staff are using algorithmic tools to help them function more efficiently, transparently and in compliance, while saving their organizations money. Right? Wrong.
- Understand the learning systems used by most financial institutions
- Learn what FIs really need to know to ensure they have the right technology
- See how Brighterion’s Smart Agents leverages its technology to provide transparent, comprehensive AI
- See the data about unsupervised learning and how it builds a more complete tool that reduces loss, increases revenue and transforms your customer service
Many banks have implemented artificial intelligence (AI) to help meet rigorous security demands but are caught in the misperception that machine learning is always AI. Get comprehensive insights from 200+ financial executives from commercial and community banks and credit unions across the US into how they leverage AI and ML.
- Learn how banks optimize their operations with learning systems
- Learn the benefits and limitations of learning solutions
- Understand how Brighterion’s Smart Agents technology takes machine learning to true AI
- Learn how unsupervised learning can drive revenue, decrease false positives, and substantially reduce manual labour