BACKGROUND

In 2016, financial institutions (FIs) filed 2 million SARs (suspicious activity reports) with FinCEN. During 2008—2018, ~90% of US government enforcement actions involved $27B in penalties.

KEY FEATURES

• Harmonizes originator & beneficiary names, addresses, bank names, etc.
• Resolves “bleeding” of data across different columns, e.g., name, address
• Classifies entities – individual, industry, government, not-for-profit, etc.
• Reduces false positives using Scry’s multidimensional approach of using ensemble of proprietary ML, NLP & knowledge graph algorithms
• Automates the creation of richer 360o profile of customers

BACKGROUND

Out of ~ 21B ACH transactions worth $46.8T in 2017, ~$1.4B were fraudulent. Financial institutions use business rules to out-sort fraudulent transactions with high false-positive rate due to constant changes in fraudster schemes.

KEY FEATURES

• Uses 500+ features to generate out-sorts
• Captures features from transactions, contact centers, user behavior, device connectivity and geolocation
• Provides “Pareto Frontier” to show trade-offs between out-sorts, precision, recall, fraud avoidance amount, etc.
• Generates out-sorts & alerts within minutes & incorporates business rules

BACKGROUND

• 1.2B mobile checks deposited annually worth $480B, 0.4% of checks are fraudulent resulting in returned checks of worth $2B
• 400% growth in losses reported over 2 years by financial institutions

KEY FEATURES

• Uses 500+ features to generate out-sorts
• Captures features from transactions, contact centers, user behavior, device connectivity and geolocation
• Provides “Pareto Frontier” to show trade-offs between out-sorts, precision, recall, fraud avoidance amount, etc.
• Generates out-sorts & alerts within minutes & incorporates business rules

BACKGROUND

• ~$16.8B lost in identify fraud in year 2017, 3.8% increase since 2016
• ~16.7M consumers were victims of identity theft, 8.5% rise since 2016
• Account takeover tripled with losses of ~$5.1B, 120% more than 2016

KEY FEATURES

• Improves detection rate and reduces false positives using Scry’s knowledge graph at various levels such as device, IP & biometrics
• Assesses real-time risk of user login by aggregating risks from customer 360o profile, fraud patterns and user activity patterns
• Improves customer experience through white box explanations
• Reinforcement learning on “its own” and via a “human loop”

BACKGROUND

Conflict of Interest occurs in investment banks & law firms because

• They cannot represent both parties in merger & acquisition
• They are often bound by “exclusivity” and related clauses

KEY FEATURES

• Creates “single source of truth” for entities, i.e., subsidiaries, affiliates, parents
• Culls out attributes from NDAs and other agreements
• Identifies news articles with negative news, CXO departures & hostile-takeover
• Determines “no conflict of interest” situations
• Provides reasons for conflict of interest & business risks
• Reinforcement learning on “its own” and via a “human loop”