Reshaping Fraud Analytics using Machine Learning

Digital Fraud and identity theft are amongst the biggest concerns for financial institutions in the day and age of evolved technologies and bots. However, with Machine Learning and AI in fraud detection, the dynamics are gradually changing, given the tools’ ability to highlight any change in the patterns using real-time data analysis.

Let us know more about how the advent of AI and machine learning is affecting fraud detection:

• Anomaly Detection; Leveraging Fraud Analytics in Machine Learning

  Recognizing any anomalies within large datasets using Machine Learning algorithms aids in identifying any unusual behaviors or any potential fraudulent activity by flagging deviations

• Analyzing Textual Data

  From emails to documents and even social media activity or any similar unstructured data, AI algorithms can verify identities and even detect scams, preventing phishing attacks

• Diverse Verification Tools

  By verifying more than one user-given information mode (facial recognition, biometrics, and any relevant personal documents), Machine Learning models add layers of security at every verification level

• Risk Scoring Technique

  Reviewing various critical factors like location of the transaction, historical data, amount, and other details, risk scores are assigned using Machine Learning fraud detection techniques and models. Transactions with alarming risk scores are investigated on a priority basis

• Adaptive Learning Models

  Designed to auto-update at regular intervals, Machine Learning models ensure that the fraud detection techniques stay up-to-date while improving continuously

• Network Analyzation

  AI uses algorithms and graphic tools to analyze different entities or accounts, uncovering any hidden connections between them. This helps keep a check on any coordinated attacks or fraud rings, ensuring enhanced safety

Enabling a Risk-free Digital World; The Role of AI and Machine Learning in Modern Fraud Analytics

Revolutionizing consumer safety across industries, AI and Machine Learning are steadfastly supporting businesses in their digital journeys, steering them towards agility through a robust digital transformation. Some notable benefits of leveraging AI and Machine learning are:

• Better Accuracy and Enhanced Efficiency

  Processing extensive amounts of unstructured and structured data is much faster with AI than with legacy software. Organizations have experienced significant error reduction in fraud identification and highlighting anomalies of any kind

• Detecting Fraud in Real-time

  AI detects and flags any unusual patterns or potential errors in no time, despite the volume of data; its ability to identify, respond, and prevent threats stands unmatched

• Cost Efficiency

  Automation in processes allows costs to go down, as there is no longer a need for manual intervention and re-checks, thereby adding financial efficiency to the business

• Better Customer Experience

  By minimizing any false positives and real-time fraud analytics, AI enables a seamless and hassle-free experience for customers while safeguarding their safety

• Fraud Prevention with Predictive Analysis

  Businesses now have access to predictive analysis using Machine Learning algorithms. This allows them to anticipate and prepare for any fraud occurrence

• Reliable Authentication Methods

  AI-driven authentication mechanisms are more accurate and foolproof than traditional verification techniques such as passwords and PINs. Stepping up with the times, AI enables advanced ways to authenticate using facial recognition, biometrics, and even voice recognition to amplify the security levels

• Better Scalability

  AI can seamlessly cater to increasing volumes without compromising on performance and reliability, allowing businesses to scale efficiently while maintaining safety and best practices for fraud detection

Diverse Sectors, United by Tech; AI Fraud Detection in Action at WNS

AI and Machine Learning have permeated all critical industry segments, making these quintessential for congruous business success and growth. Some key sectors where WNS has leveraged technological advancements are:

• Banking and Financial Services

  Monitoring transactions in real-time to prevent any phishing fraud, identity theft, payment fraud, and credit card fraud. By blocking any suspicious transactions and adding extra layers of security, AI models have ensured improved safety. WNS has powered leading names within the BFSI sector to achieve exceptional outcomes by deploying advanced AI and Machine Learning models

• Insurance

  With rigorous analysis of consumer claims data, AI has helped insurance companies spot any inconsistencies and identify any false claims or staged incidents. Additionally, insurance providers also make use of graphic analytics to visualize and detect any complex fraud. Leveraging AI-driven fraud detection solutions WNS has revolutionized the various stages of insurance process, enabling risk-free, accurate and quick decision-making

• E-commerce and Retail

  From highlighting fake websites to detecting suspicious purchase patterns, profile changes, etc. AI leverages consumer and device data along with the history of transactions to flag abnormal activity and potential scams

• Cybersecurity and Telecommunications

  AI facilitates consistent monitoring of user behaviour and network activity through which telecom companies block fake calls and detect SIM frauds, besides scanning any phishing emails/calls to keep consumer data safe

• Healthcare

  Using anamoly detection, AI helps to identify irregular billing, any unauthorized access to data, and even fake claims. This allows healthcare providers to protect financial data as well as patient privacy

How WNS Enabled and Digitally Secured Businesses for its Clients

Harnessing the power of AI and ML, combined with deep domain expertise, WNS has been curating customized solutions using proprietary models for global clients in the following key areas:

1. Managing Client Lifecycle

   By orchestrating processes for digital identity verification, automating data extraction, and integrating it with public or third-party sources, WNS streamlines the client onboarding process and Know Your Customer (KYC) processes

2. Mitigating False Positives in Anti-Money Laundering (AML)

   Algorithms generated through Machine Learning analyze AML platform alerts, which are based on their significance. Effectively suppressing low-risk false alerts, this prioritization method also enables threshold tuning that reduces the false positives

3. Investigation Narrative Automation

   AI models and advanced analytics create a holistic view of consumer KYC profiles, facilitating more accurate and automated narratives for investigation outcomes. To identify any anomalies, the fraud detection models observe and analyze everything from transaction history to critical account details and customer relationships

4. Enhanced Screening and Investigation Processes

   Internal and external data comparison through AI/ML models enables a detailed analysis for investigating hits. To evaluate and scrutinize hits, we deploy Advanced Natural Language Processing (NLP) and sentiment analysis

5. Link Analysis for Complex Investigations

   Complex investigations using historical data help establish connections between entities and individuals. Using this technique, any hidden patterns in transactions are highlighted, generating warning signs to prevent fraud and money laundering

6. Detecting Fraud in Claims Processing

   A leading Insurance client benefited through WNS’s AI-driven automated claims processing solution, leveraging SKENSE (our proprietary platform). Enhancing the overall customer experience, the solution also reduced the settlement cycle time by over 60%, along with a 50% increase in data labelling productivity

Envisioning a Tech-First, Smart Future

As the AI and Machine Learning advancements continue to evolve, combatting fraud will foresee futuristic trends such as blockchain integration to create unbreakable transaction records and dynamic risk scoring models that adapt to changing user behavior and technology for accurate detection. However, at the heart of this evolution will stand cross-industry collaborations to share and understand the existing as well as emerging threats for swifter, stealthier responses.

WNS is at the forefront of this transformation, offering comprehensive suites of fraud management by leveraging its fraud analytics and machine learning technology. To know more about our capabilities and offerings, connect with our experts by clicking here.