AutoML: How AI Could Become more Accessible to Banks and Financial Institutions

The difference between success and failure in the banking industry depends on risk management. Banks are under pressure to provide better and more exceptional customer services to stay competitive. At the same time, banks face challenges from growing governance, regulatory frameworks, increasing costs, customer defaults, crimes and frauds, and so on.

Be it retail lending, commercial banking or wealth management, and banking, customers expect banks to have tailored solutions that are applicable to their unique use-cases. To offer personalized products and services across multiple channels and with universal access, banks should be able to leverage the available data to make a prediction about the evolving needs of clients, products and services of their choice, and preferred mode of interaction.

 

How AI can help banks

 

The promise of artificial intelligence (AI) sank in across industries in recent years. To deliver banking services and optimize their client portfolio offerings, banks can leverage the power of AI, powered by data science acceleration.


Customer Experience

 

With AutoML and data science automation, banks can implement AI in banking to offer excellent customer service:

  • Build predictive models to identify customer profiles for specific products and services.
  • Satisfy customer demands and strengthen relationships.
  • Identify new opportunities.
  • Leverage your customer support know-how.
  • Identify client preferences and sensitivity to pricing.

 

Customer Lending

 

The COVID-19 pandemic has made customer lending a riskier proposition. AutoML can create a better value proposition in different ways.

  • Build accurate credit models to analyze risk.
  • Identify your attractive business proposition based on risk-adjusted returns.
  • Create a lending portfolio for maximum return.
  • Identify factors that lead to default and intervene proactively.
  • Build loss-forecast models to manage risk.

 

 

Investment management

 

Financial portfolio management needs due diligence and deep insights into market opportunities. Artificial Intelligence can help business experts to identify key trends to maximize returns.

  • Optimize execution and trade routing.
  • Managing investment opportunities for investors.
  • Analyze market and identify key trends.
  • Lower transaction costs by reducing errors.

 

Use cases of AI in banking

 

The application of machine learning in baking varies based on the banking business. Here are the implementations of AI in multiple banking services.

 

For credit monitoring and management

 

Proper credit management can help banks to move from loss to profitability. Banks implement machine learning models to figure out the factors that lead to defaults and other types of losses. Using machine learning models helps create a subtle approach to client assessment, credit approval, pricing, and portfolio management that offers the best results for customers while mitigating risk for the bank. In partnership with H2O.ai (Award-winning AutoML platform), ACT21 Software helps banking and financial institutions build granular models using AutoML and data science.

 

 

Fraud and financial crime monitoring

 

Bank frauds reported by banks and financial institutions shot up 159% in 2019-20. Financial crime monitoring is a critical part of managing the safety of a bank. Banks use machine learning to identify the patterns of money laundering and prevent fraud by leveraging data from the previous investigation to design models that accurately identify the suspicious acidity and alarm bank officials in real-time.

 

For improving banking client experience

 

Modern banking clients expect banks to offer them customized solutions as and when they need them. Implementation of AI and machine learning in banking can help banks predict client behavior and demands. Banks leverage client data about satisfaction and complaints about customer churn prediction and to take action to prevent attrition. ML models can be used to predict branch traffic volumes and to understand where and when new branches are needed.

 

Financial product demand forecast

 

Understanding the need for launching the right product at right time is a critical part of running a successful business. Machine learning and AI in finance can help banks forecast demands for loan types, mortgage rates and other financial products. It also helps understand cash flow requirements to run an organization, a branch and even at the ATM.

 

Understanding and managing investments

 

Because of the turmoil economic situation, understanding and managing investments can be challenging and time-consuming. With ML and AI, banks can optimize financial investment by leveraging historical transactional cost analysis execution data. Al enables banks and financial institutions to create new-age decision-support systems that optimize market conditions while ensuring compliance.

 

Accelerate your business with AutoML and data science automation

 

To compete with tech giants and start-ups, traditional banks and financial companies may find it hard to retain the best practicing data scientists and hire the newest crop of graduates. Automated machine learning (ML) tools, commonly known as AutoML, can fill the data science talent gap and increase the efficiency of analytics teams.

The evolution of AutoML tools is radically changing the perception of data science, making it accessible to business experts with basic data-science skills, rather than the team with experienced data scientists.

Many organizations analyzed that a data scientist spends 60 to 80 percent of his time preparing the data modeling. Once the initial model is ready, only a fraction of the data scientist’s time is spent on testing and tuning code.

The objective behind autoML is to automate all data preparation, as well as modeling and tuning steps, so as to eliminate the manual technical work. AutoML don’t automate everything now, but they are currently able to create machine learning models that deliver good returns.

Essentially, tuning model parameters is a commodity, and performance is influenced by data selection and preparation. Though to what extent AutoML can automate modeling tasks remain unclear, complete automation still seems far away. Be that as it may, it is certain that automation of modeling tasks will make data science more accessible to business experts.

 

Best AutoML tool for Banking and Financial Institutions

ACT21 Software brings cutting-edge innovation to the banking and financial industry by providing reasoning and transparency into AI lead decisions. In partnership with H2O.ai we present to you an internationally acclaimed, award-winning Automatic Machine Learning (AutoML) platform – Driverless AI

On average, 40% of companies take more than a month to deploy an ML model into production. H2O’s Driverless AI empowers data scientists to work on projects faster and more efficiently by using automation to accomplish key machine learning tasks in just minutes or hours, not months

Connect with us at abhishek.roy@act21softwares.com to explore the AutoML platform.

 

Final words

 

As the power of artificial intelligence has been realized across industries, banks need to review their talent strategies to gain the skills required to deploy and excel AI system. However, scaling up a data science process is challenging, time-consuming, and expensive. AutoML empowers data analysts, software engineers and BI team to build and benefit from predictive models. Leveraging automation to expedite the development of models gives data scientists the freedom to be more productive.

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