# Attacking Banking and Fintech Fraud Head-On Through AI-Infused Strategies  Author: Pal Sinha, Barnali Author URL: https://financedigest.com/author/pal-sinha-barnali Published: 2022-11-24 Category: FINANCE Category URL: https://financedigest.com/category/finance Meta Title: How AI is Revolutionising Fraud Prevention in Financial Services Meta Description: Discover how AI is revolutionising fraud prevention in financial services, with new research showing a rise in financial crime. Find out how advanced machine URL: https://financedigest.com/attacking-banking-and-fintech-fraud-head-on-through-ai-infused-strategieshtml ![undefined](https://prod.superblogcdn.com/site_cuid_cm5qst7v3003gwirgwqtxn8i8/images/istock-1324440713-1736814821253-compressed.jpg) ![](https://prod.superblogcdn.com/site_cuid_cm5qst7v3003gwirgwqtxn8i8/images/carol-hamilton-450x599-1736814821261-compressed.jpg) _By_ **_Carol Hamilton,_** _Chief Growth Officer at Provenir_ In all sectors, and especially in the financial services sector, there is a need to protect against “the great unknown” – that is the first interaction with a customer, which is always the riskiest, with the highest potential for fraud. New [research](https://risk.lexisnexis.co.uk/about-us/press-room/press-release/20221006-banks-look-to-risk-orchestration-technology-to-combat-fraud) shows that 43 percent of financial services organizations expect the cost-of-living crisis to increase the risk of financial crime and fraud over the next 12 months, as scammers target vulnerable consumers struggling with rising bills. Fraud detection and prevention are at the top of the list of reasons why banks and fintech providers are deploying [artificial intelligence](https://www.financedigest.com/types-of-artificial-intelligence.html "artificial intelligence") (AI). According to a recent [survey,](https://www.businesswire.com/news/home/20220307005541/en/Only-18-Percent-of-Fintechs-and-Financial-Services-Organizations-Believe-Their-Credit-Risk-Models-Are-Highly-Accurate) 78 percent of financial executives cited fraud prevention as a key driver in the adoption of AI-enabled risk detection in the past year. Additionally, 65 percent of respondents said improving [fraud detection](https://www.financedigest.com/the-clues-to-detecting-fraud-are-hidden-in-relationships.html "THE CLUES TO DETECTING FRAUD ARE HIDDEN IN RELATIONSHIPS") and prevention is one of the primary reasons for using alternative data in risk analysis. As financial [fraud and risk](https://www.financedigest.com/how-can-you-reduce-the-risk-of-fraud-in-b2b-payments.html "How can you reduce the risk of fraud in B2B payments?") vectors are constantly evolving, accessing real-time data and applying it to the latest defensive measures in a fully automated fashion makes AI ideally fit for the fight. Currently there are solutions available that will automate this process, using automatic testing to select the most effective models for the business [problem an organization is trying to solve](https://www.financedigest.com/british-start-up-reveals-the-secret-to-solving-the-problem-of-identity-fraud.html "British start up reveals the secret to solving the problem of identity fraud"). For example, there may be 30 models that could show positive results, and to pick the right one, the automation will run through these models to show which model or combination of models is most effective based on the specific needs, such as lowering delinquency rates or supporting more inclusive lending; and when it comes to [fraud prevention — reducing](https://www.financedigest.com/the-importance-of-a-proactive-and-collaborative-approach-to-reducing-contactless-fraud.html "THE IMPORTANCE OF A PROACTIVE AND COLLABORATIVE APPROACH TO REDUCING CONTACTLESS FRAUD") risk and preventing significant losses. However, deploying models can be daunting – 47 percent of executives find it difficult to integrate cognitive projects into [existing processes and systems](https://www.financedigest.com/solvency-ii-pushing-existing-systems-and-processes-harder-is-not-enough-for-compliance.html "Solvency II: pushing existing systems and processes harder is not enough for compliance"). When/ if they get deployed, performance monitoring is often limited and not real-time, meaning that when the models drift, the reduction in their effectiveness isn’t noticed or addressed as soon as it should be. This directly impacts their ability to make accurate predictions. Even more, traditional policy-based approaches often fail to identify potential [fraud and can produce large volumes of false positives](https://www.financedigest.com/allianz-benelux-taps-into-modern-data-management-to-combat-fraud-and-foster-a-positive-customer-experience.html "Allianz Benelux Taps into Modern Data Management to Combat Fraud and Foster a Positive Customer Experience"), which then require manual review. What’s [needed is a new approach to improve](https://www.financedigest.com/struggling-banks-need-to-modernise-and-improve-software-quality-management.html "Struggling Banks Need to Modernise and Improve Software Quality Management") the speed and accuracy of fraud decisions without producing large volumes of false positives. A more enlightened approach involves leveraging optimized contextual scorecards, machine learning algorithms and outlier detection — all [types of AI-infused strategies to improve fraud](https://www.financedigest.com/types-of-fraud-in-e-commerce.html "TYPES OF FRAUD IN E-COMMERCE") detection and accuracy. AI enables organizations to build and monitor predictive, explainable and scalable advanced machine [learning models](https://www.financedigest.com/learning-from-the-disrupters-how-finance-brands-can-build-transformative-business-models.html "Learning from the disrupters – how finance brands can build transformative business models") to predict fraudulent applications. Depending on [business requirements and data](https://www.financedigest.com/reckon-introduces-link-to-bank-data-to-help-small-businesses-streamline-the-reconciliation-process.html "Reckon introduces link to bank data to help small businesses streamline the reconciliation process ") availability, both supervised learning and unsupervised learning approaches can be used. **Supervised Learning** Supervised learning involves traditional scorecards and [machine](https://www.financedigest.com/make-time-to-reshape-the-finance-function-with-automation-and-machine-learning.html "Make time to reshape the finance function with automation and machine learning") learning. Traditional scorecards enable organizations to learn complex relationships from identified fraud to then [predict](https://www.financedigest.com/2021-predictions-less-fraud-shifting-consumer-behaviour-and-accelerating-pace-of-innovation.html "2021 predictions: less fraud, shifting consumer behaviour and accelerating pace of innovation") fraud. With machine learning, advanced [analytics tools including graph databases](https://www.financedigest.com/oracle-offers-its-mysql-heatwave-database-and-analytics-on-amazons-cloud.html "Oracle offers its MySQL HeatWave database and analytics on Amazon’s cloud") are used to discover unknown key relationships, interactions and indicators. By doing this, organizations can identify patterns too complex for traditional scorecards to detect. **Unsupervised Learning** Unsupervised learning includes outlier detection which learns from patterns and identifies aberrance. With outlier detection, [businesses can look for new and emerging types](https://www.financedigest.com/types-of-business-loans.html "Types of business loans") of fraud by identifying outlier behavior and utilize tags to differentiate fraud and non-fraud. Similar to supervised approaches, this enables organizations to uncover potential unknown [risk factors and immediately mitigate any impact](https://www.financedigest.com/russia-could-hike-rates-in-2023-if-inflation-risks-have-big-impact-cenbank.html "Russia could hike rates in 2023 if inflation risks have big impact -cenbank"), where necessary. **Key Elements in an AI-Infused Approach** There are a few key elements in an AI-infused approach to [fraud prevention:](https://www.financedigest.com/payroll-fraud-and-how-to-prevent-it.html "Payroll fraud and how to prevent it") - **_Diverse Data:_** By leveraging traditional and alternative data, organizations can improve model accuracy, while reducing bias and promoting [financial inclusion](https://www.financedigest.com/why-banks-could-stand-to-learn-a-thing-or-two-about-financial-inclusion-from-fintechs.html "Why banks could stand to learn a thing or two about financial inclusion from fintechs"). An organization’s data is no longer enough, supplementing with third-party data and intelligence [sources adds uplift in accuracy and power](https://www.financedigest.com/powered-by-poo-ten-weird-and-wonderful-alternative-energy-sources-that-could-soon-be-powering-our-homes-2.html "POWERED BY POO: Ten weird and wonderful alternative energy sources that could soon be powering our homes") of detection. - **_Model Selection:_** This involves choosing the most appropriate algorithm (Gradient Boosting Decision Trees, Random Forests, Deep Neural Networks, etc.) depending on the nature of the dataset, and the use case. Organizations without data science sophistication can leverage automated [model development approaches](https://www.financedigest.com/new-charitable-commission-sharing-model-is-for-lifeprice-comparison-website-launch-bolstered-by-innovative-approach.html "New charitable commission-sharing model is for lifePrice comparison website launch bolstered by innovative approach") where low-code user input suffices. - **_Explainability:_** This entails fulfilling AI transparency expectations for regulation, audit and clear [business practices](https://www.financedigest.com/exact-launches-practice-management-solution-to-help-transform-accountants-into-trusted-business-advisors.html "Exact launches Practice Management solution to help transform accountants into trusted business advisors"). Adopting LIME and SHAP explanation techniques, as examples, enable [users to understand how and why a model](https://www.financedigest.com/the-iphone-x-factor-over-a-quarter-of-smartphone-users-plan-to-upgrade-to-a-new-iphone-model.html "The iPhone X Factor: Over a quarter of smartphone users plan to upgrade to a new iPhone model") has made a certain prediction. Similarly, without data science sophistication, organizations can seek solutions with these approaches embedded, showing the end user the business-interpretable output in interfaces and dashboards. - **_Scalability:_** Organizations can reduce the development [time from months to days](https://www.financedigest.com/love-in-a-time-of-inflation-how-much-will-valentines-day-set-you-back.html "Love in a time of inflation: how much will Valentine’s Day set you back?") and automatically train, test, monitor and manage data models through sophisticated and accessible DevOps for machine learning, known as “MLOps.” To be successful with an AI project, an organization needs an MLOps solution that simplifies the deployment, monitoring, and retraining of data models. This is something that an organization can build internally, however [partnering with an external resource is a cost-effective option](https://www.financedigest.com/nexi-open-to-options-as-banco-bpm-seeks-partner.html "Nexi open to options as Banco BPM seeks partner"). By [adopting AI-infused strategies](https://www.financedigest.com/high-quality-low-voltage-motors-market-recent-industry-developments-and-growth-strategies-adopted-by-players.html "High Quality Low Voltage Motors Market: Recent Industry Developments and Growth Strategies Adopted by Players"), organizations can transition from traditional policy-based approaches to those that leverage predictive, explainable and scalable machine learning algorithms. This helps radically improve the speed and accuracy of fraud decisions, to [meet banking and fintech](https://www.financedigest.com/fintech-incubation-more-value-than-meets-the-eye.html "FINTECH INCUBATION: MORE VALUE THAN MEETS THE EYE") fraud head-on. **About the Author** Carol Hamilton is Chief Growth Officer at [Provenir](http://www.provenir.com), which helps fintechs and financial services providers make smarter decisions faster with its AI-Powered Risk Decisioning Platform. Provenir works with disruptive financial [services organizations in more than 50 countries and processes more than 3 billion](https://www.financedigest.com/20-billion-in-bank-service-fees-are-you-overpaying.html " Billion in Bank Service Fees: Are You Overpaying?") transactions annually. --- This blog is powered by Superblog. Visit https://superblog.ai to know more. ---