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BOOSTING RISK COMPLIANCE THROUGH SYNTHETIC DATA GENERATION
A newly set up financial institution looking to build a strong corporate book might lack data for portfolio and events that are inputs for its risk model . How can it extrapolate and model scenarios without sufficient data , and that is where Synthetic Data Generation comes into play , explains Ahmet Cenk at SAS .
Every business decision comes with risk . Yet , the reality is that without risk , there is no reward . What matters most for organisations is to find trustworthy ways to navigate uncertainty , manage risk with confidence , mitigate threats , and capitalise on unforeseen opportunities . It all comes down to risk management and intelligent risk analytics .
Having the capacity to make fast and accurate risk decisions can dramatically change business outcomes . This is true for any organisation and especially for entities that extend credit , from traditional banks to fintech , car dealers , mortgage companies , communications providers , government agencies , health care systems , insurance companies , credit management services and even retailers .
AI and ML systems excel in recognising patterns in data to make predictions .
Given the unpredictable global financial environment , regulators are urging banks to identify risk exposures , as an effort to strengthen the resilience of the financial system .
Risk management
The Central Bank of the UAE , CBUAE recognizes risk management , along with internal audit and compliance , as key control functions in a bank . The
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