Quantitative modelling and analytics (popularly called quant) is used widely in the financial services industry.
By leveraging quant, financial service providers analyze trends and forecast diverse business variables and metrics such as income, cost, asset valuation, market volatility, risk probability, liquidity, and price. In the financial services industry, the adoption of quant is on the rise in areas such as automated investment management, robo-advisory, non-linear investment and trading strategies, and risk management and compliance. In recent years, quantitative modeling is also being widely used in market and credit risk assessment, asset-liability management, liquidity management and financial crime prevention.
Quant led by data science
The data deluge has driven the adoption of big data and data science techniques as firms have started exploring alternative data for crucial insights.
Data science has also altered the profile of functional, silo-centric quant, which traditionally relied on hard, structured data alone. Moreover, financial services firms need multidisciplinary expertise to harness unique insights from varying sets of soft information such as non-financial, contextual data. Such qualitative and non-formal data insights are key to incisive business decisions.
The AI-driven paradigm
Quant augmented by data science and artificial intelligence (AI) creates new possibilities.
It enables financial services firms to quickly design customized products and enhance resilience. Integrating data science capabilities into quantitative modeling opens new ways of analyzing many hard-to-define, human emotion-based concepts such as sentiment, behavior, and bias. This enables detailed evaluation of multifaceted factors involved in vital business decisions.
BFSI functions can benefit tremendously from data science and AI-driven quant. Advanced features of quant help simplify the analysis of complex issues such as environmental, social and governance (ESG) aspects, climate risk, customer engagement, and conduct risk, adding unconventional advantages. Other areas that will benefit include credit assessment, default and delinquency prediction, operational efficiency, predictive and prescriptive screening of prospects, and credit decisions. Wide-ranging market and credit risk simulation, pricing estimates, sensitivity models, and hedging can further strengthen resilience. Additionally, banks can enhance fraud management and financial crime prevention through proactive identification of anomalous behavior patterns and market abuse, helping them adopt a preemptive stance in risk mitigation.
Further, financial services firms can improve customer engagement by leaps and bounds through next-best action recommendations and analysis of customer advocates and their referral networks. This will open new avenues for contextualization, greatly enhancing the customer experience. Similarly, quant reinforced by data science and AI will help banks leverage non-market insights for portfolio allocation and trading strategies. It will also help banks to take advantage of non-conventional assets and market-focused opportunistic investment options. By deploying quant bolstered by data science and AI, financial services firms can greatly improve liquidity and cashflow forecasting, prediction of delays in receivables, regulatory compliance, and prevention of transactional failures and exceptions, in turn, enhancing overall operational efficiency.