Finance and Accounting
The transforming power of AI in the finance industry
Technology-driven disruption has impacted just about every industry. In finance, automation and blockchain were just two of many significant disruptive phenomena.
Digital transformation and the pace of technological innovation have been significantly increasing and have caused traditional financial institutions to take stock and adopt technologies like artificial intelligence (AI) and big data in their business strategies to maximise insights, efficiencies, and gains.
AI is greatly impacting the way financial institutions deliver quality products and services to a rapidly changing audience. According to a recent survey, 84% of enterprises believe AI has the potential to create and sustain a competitive advantage, while 23% have integrated AI into core processes, products, or services.
About 70% of financial services firms are also using machine learning (ML) to predict cash flow events, fine-tune credit scores, and detect fraud.
AI and ML transforming the finance and banking industry
Strategic AI implementation has delivered enhanced operational efficiency, improved management decision making, and hyper-personalised customer experiences for the finance sector. Here are a few ways AI and ML are transforming the finance industry:
Credit scoring and risk assessment:AI can accurately scan data and records to help assess risks associated with loans and credit disbursements. It analyses the client’s past credit history, as well as payment behavioural patterns and data, to draw accurate conclusions about the creditworthiness of the borrower. With predictive analysis capabilities, AI also delivers a clear picture of future market trends and conditions that help in making timely decisions and preparations.
Superior customer experience:The adoption of AI results in businesses exploring new ways of providing convenience, comfort, and increased benefits to customers, such as:
- Quick problem solving using round-the-clock chatbots.
- Personalised banking experience through ML, which helps develop customised plans and personalised offers based on interest, credit score, or past behaviour.
- Optimising day-to-day functions such as digital transactions and onboarding, thereby improving customer service and retention.
Algorithmic trading:AI and ML tools can be powered by fast processing to help traders make rapid decisions. Algorithmic trading systems cut short computation time and help financial companies keep track of demands for rapid pricing as well as calculate risks and fine-tune financial portfolios through deep learning.
Compliance and data:AI-driven analytics helps in regulatory compliance to solve common challenges such as regional sanctions or court compliance terms as well as evolving IT environment and system issues. It also helps financial institutions manage massive volumes of data, creating a strong data foundation to rely on.
AI-driven analytics supports informed financial decisions and services
To make well-informed, sound, and risk-assessed decisions, financial companies depend on meticulous and accurate information through high-quality data. The aim of AI-driven analysis in various finance use cases is to infuse AI in data processing that provides algorithms better decision intelligence. Combining data, analysis, and decisions to create an efficient and proven system is imperative for financial institutions in the coming years.
It is clear that AI is propelling new business models and reshaping workforce environments for the future. CFOs need to be proactive and chalk out dynamic financial plans that include generative AI implementations for optimal returns.
How Infosys BPM can help?
At Infosys BPM, we leverage AI to solve real-world problems for the CFOs.
With over 20 business solutions built specifically for finance and accounting, we have helped our clients improve end-to-end business metrics. We help CFOs to craft their digital strategic journey to become an intelligent enterprise facilitating ‘real-time’ finance and boast of:
- 15–18% improvement on OTP (on-time performance)
- 50% reduction in GL (general ledger) coding effort
- 30–45% reduction in transaction cost for collections
- 33% reduction in close cycle time
- 20–25% reduction in DSO (days sales outstanding)
- 100% compliance on ICA (inter-creditor agreement)
Learn more about our digital finance solutions.