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Using ANAI, citizen data scientists and other developers with limited machine learning expertise can easily ingest data, generate insights and build/train ML models.
Empower financial institutes through AI-powered credit scoring methods giving an ability to avoid bad debts and defaulting customers while credit lending.
AI systems can determine fraudulent transactions before they actually occur by analyzing them and can help banks prevent frauds such as money launderings and fake identity sign-ins.
Automate repetitive processes such as documentation, report creation, or keeping track of share market prices for a financial institute using AI, ultimately saving extra time and costs.
Deploy AI-based tools to handle reconciliation, helping financial entities manage data and perform these tasks more efficiently in an error-free way saving a lot of time.
The entire process is quite expensive if hiring the proper talent and building and maintaining such complicated systems are involved.
Even if the model shows the best performance, customers and other stakeholders can still have trust issues with the system which can lead to a failure in adoption.