AI is increasingly adopted by financial firms trying to benefit from the abundance of available big data datasets and the growing affordability of computing capacity, both of which are basic ingredients of machine learning (ML) models. Financial service providers use these models to identify signals and capture underlying relationships in data in a way that is beyond the ability of humans. However, the use-cases of AI in finance are not restricted to ML models for decision-making and expand throughout the spectrum of financial market activities (Figure 2.1). Research published in 2018 by Autonomous NEXT estimates that implementing AI has the potential to cut operating costs in the financial services industry by 22% by 2030.

This section looks at how AI and big data can influence the business models and activities of financial firms in the areas of asset management and investing; trading; lending; and blockchain applications in finance.

FintechWE Member - Ms. Dilek Duman comment : 

I would like to add some other application areas to this valuable chart.
More use cases for AI:
- workforce optimization : for Back office, Middle office and Call center; in order to use the workforce efficiently 
- Target Setting : KPI, Sales targets; in order to support growth 
- Cash Flow Optimization : for branches, ATMs
- Proactive servising to Customers from digital channels
- Optimization of physical channels

Sales Target Setting for:
- branches 
- portfolio managers
You know setting correct targets for branches/PMs are not easy. If a branch is successful next year the headquarters gives more aggressive targets for this branch but if the branch result is not so great next year this branch’s target is not so high. Which leads inequality, demotivation and loss of opportunity. So this project has to be lead by business and HR together. With help of AI and AI experts very successful results could be achieved.

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