TRENDING
It reproduces the same statistical properties , probabilities , patterns , and characteristics as the real-world data from which it is trained , while it can also generate data at or beyond the boundaries of the original data , potentially representing novel data that would otherwise be neglected .
Using GANs for SDG , together with reliable risk management solutions , financial institutions can gain a significant competitive advantage . For example , they could synthetically generate default cases when realworld data is limited . Data quality is another challenge that can be mitigated with SDG , since the available data is not always satisfactory .
SDG can produce entire sets of data that share the same characteristics with the real data , only without compromising the privacy of their customers .
Ahmet Cenk , Senior Business Solution Manager , SAS
Benefits of SDG
Recognising the value of synthetic data early on , Digital Dubai has launched a framework for its use in October 2022 . Gartner predicts that by 2026 , 75 % of businesses will use GenAI to create synthetic customer data , up from less than 5 % in 2023 .
SDG provides more cases and eliminates issues that existed in the original data , ensuring high data quality , which in turn leads to better performance in missioncritical areas , including improved credit scoring accuracy and reduced decisioning bias .
The use of synthetic data can further help organisations secure the privacy of their real data . There are many reasons why a financial institution would rather not risk using real customer data in the process , such as the involvement of third parties in model development , the use of cloud services , and regulatory concerns , to name a few .
Some SDG techniques can be used to create data for simulations or hypothetical scenarios . Banks can simulate once-in-a-generation Black Swan events using sparse data sets , train models on new exogenous developments including climate change , and enhance micro , macro-economic and market condition simulations to determine potential risks before they become challenges .
Furthermore , in the field of fraud and financial crime detection and prevention , financial institutions can use synthetic data to extrapolate from rare events and anomalies to train models on specific fraud and
anti-money laundering topologies . Synthetic data can also be useful in penetration testing of existing fraud control systems to fine-tune them for optimised defensive capabilities .
Today , organisations are looking for new ways to
leverage AI to improve operational efficiencies and maximise overall outcomes . p
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