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Banking and Financial Services

AI / ML Applications and Use Cases in Banking and Financial Services Industry

Gestalt brings pre-built AI analytics enabled solutions for high impact areas within the banking sector. With an eye on making sure our clients can institutionalize AI/ML solutions, Gestalt brings to every project well defined frameworks, templates and best practices which accelerate the delivery and adoption of AI enabled solutions.

Addressing Key Issues Across Banking and Financial Services (BFS) Industry

Some of the biggest challenges BFS sector is facing include:

  • Fraudulent Transactions
  • Anti-money laundering (AML)
  • High Customer Attrition
  • Credit Risk Management

We address these challenges using advanced Anomaly detection and Transparent Machine learning capabilities of EazyML. Here we detail our several proven point solutions & use cases for putting these technologies to practice.

Fraud detection and prevention

Use EazyML driven machine learning algorithms to analyze historical records, behaviors and use the insights to monitor transactional parameters leading to:

Intelligently enhancing library of rules for fraud detection :

EazyML ensures that the rules, traditionally based largely on expert systems, are updated regularly to keep up with the constantly evolving and dynamic fraud attempts. It helps reduce false positives, does real time fraud analysis, increases automation for fraud prevention, not to mention speed, quality and cost effectiveness.

Go beyond the rules using anomaly detection for real time transaction monitoring:

EazyML applies anomaly detection technique, where it looks for unusual or more sophisticated fraud, which are often not addressed in expert driven rules engines. It brings confidence score to the insights, helps consider those that are accurate (high confidence score) and ignores those that are not (low confidence score)

Automate the effort of fraud analyst

To supplement & enhance the effort of the fraud analyst, EazyML continuously optimizes the fraud detection system through reviewing, labelling data, getting insights and expressing them as simple rules, which the experts can compare with their existing rules to tune them for predictive analytics. So that there is no requirement of labeled data, instead learns in an unsupervised way about what’s the norm, and once it builds that, anything that deviates from it is alerted as fraud.

Underwriting & Management of Credit Risks

Gestalt solutions built around EazyML enable banks to lend to more without increasing the risk appetite and grow its commercial lending operations. It helps identify and mine alternate data which complements core financials, to better assess credit risk, allowing the bank to lend to some deserving thin-file candidates. Large banks in North America have used our solution to increase productivity, reduce defaults and lend to more – by about 8-13%. These solutions have also helped our clients with …..

  • Better assessment of risk for loans – by helping identify additional data to augment the financials. Reduced default rate by 5-15%. It augments, not replaces, the existing risk assessment process.
  • Increase top-line growth – by helping approve loans to those in the gray zone, which were barely declined.
  • Self-calibrate the machine to become better at identifying defaulters.
  • Help monitor the loan for its life cycle for proactive alerts about risk exposure.

Customer churn prediction

 Apply EazyML AI & Advance Analytics to bank’s customer data and derive insights on the churn patterns. Specifically which customers are most likely to defect, reasons and potentially the time frame.

Build a predictive model and execute a learn, test and predict cycle to iteratively refine the insights for actionable intelligence.

Sustainable Automation for action on alerts:

Prioritize your alerts from existing AI/ML models. Alerts are usually accurate from the complex models refined over time, but do not necessarily inform how much the alert can be trusted, nor do they provide reasons behind the alert – so as to make the alerts relatable to the experts and win the confidence of operations. EazyML’s explain-ability score prioritizes the alerts, its reasons make operations trust it. Some of the largest Financial Services firms use EazyML for bringing this sophistication to their existing AI/ML systems.

Additional Use Cases

  • Cost reduction of about 10-15% for underwriters by automation
  • Reduced customer acquisition cost by 15-20% by pruning the applicants from digital marketing campaigns. Targeted marketing for business solicitation
  • Compliance and regulation – transparency of decisioning. Information elements reported – government registration and risk analytics, for instance – pass the requirements of adverse action, compliance and have no bias.