Industry Trends on Retail Banking
Blogs
Industry Trends on Retail Banking
The pandemic has accelerated changes in the retail banking industry, reshaping the competitive landscape, disrupting traditional banking areas such as branch banking and speeding up digitalisation. Lenders are adopting new strategies to overcome current challenges such as the low interest rate regime, the slow response time to new loan applications, high IT investment costs and regulatory compliance requirements. The global banking industry will continue to be buffeted by challenging macroeconomic headwinds in 2023, and a keen focus on resilience and innovation from retail banks is anticipated in response.
With a continuing emphasis on customer centricity, banks will augment digital capabilities and leverage data analytics to deliver hyper-personalized services and superior CX. Exploration of partnership and M&A opportunities with FinTechs may be on the rise; and cybersecurity, regulatory developments, and sustainability concerns will all play a role in strategy and decision-making as the retail banking sector moves forward.
Continued rise of fintech lenders
Legacy banks’ slow and cumbersome loan processing setups are being surpassed by the faster and easier online processes of fintech lenders or “neo-banks”. The rise of such “digitalonly” lenders is fuelled by a combination of megabanks’ bureaucratic processes, physical customer interaction not being required, due to the pandemic, and the easy availability of IT infrastructure. This has reduced the number of barriers to entry in the retail lending industry. A related study found fintech banks 1.8x more able to innovate and offer personalised products than legacy banks. Allied Market Research expects the global fintech lending market to grow at a CAGR of 27.4% from 2021 to 2030 to reach USD4.95tn by 2030. Fintech lenders’ digital-only presence keeps operating costs low, while innovative marketing techniques enable low-cost customer acquisition. For instance, fintech lenders have targeted niche groups that were underserved by big banks’ “one-size-fits-all” marketing strategy. Paybby, Fortú and Cheese are fintech startups that cater to the Black, Latino and Asian-American communities, respectively. Niche online lenders also exist to solely finance, say, automobile purchases or education loans. Specialised financing products such as “Buy Now, Pay Later” have gained sufficient popularity to replace credit cards and personal loans. These lenders may offer additional services such as insurance, payments and checking accounts to further encroach on legacy bank territory.
Data-driven credit risk models
Credit scoring models relying only on credit bureau-generated scores, such as FICO scores or VantageScores, are being supplemented with data analytics models to provide reliable alternative scoring systems. Credit decisions are increasingly based on behavioural data (spending patterns, bank account patterns, psychometric test data), personal data (such as level of education, occupation and number of years in current job) and income and asset data (such as stability of cash flow, property ownership and timely payment of rent and utility bills). Thus, thousands of data points can track not just the borrower’s ability to pay, but also their willingness and discipline to settle debts. For example, ZestFinance is a machine learning, risk-predicting platform that auto lenders (including Ford Motor) have used to reduce losses considerably. Traditional credit bureaus such as FICO and Experian have started offering credit scoring models (e.g., UltraFICO and Experian Boost) that incorporate a borrower’s transactional information using utility, telephone and television bill payment data, along with their deposit account information. Individuals denied access to lending products due to a lack of conventional credit scores may be able to avail themselves of credit based on these data-driven scoring models. However, concerns over the privacy of a consumer’s personal information may be an impediment for such models.
Greater use of technology
The biggest cost savings banks can make are in using process digitisation and automation to replace inefficient and manual processes in middle and back offices, according to KPMG. Banks in North America alone could save over USD70bn up to 2025 by leveraging technology. Technology was initially only meant to replace repeatable, “high volume, low value” tasks. However, artificial intelligence (AI), machine learning (ML) and automation are adding extra value at every step of the lending process. Some common examples are personalized AI chatbots, ML-enabled tools for KYC and data analytics to assist in loan pricing.
Conclusion
It is imperative for retail banks all over the world to maintain pace with the latest developments in the banking world. Especially in the time of WFH, there is a tendency for retail customers to choose lenders that go beyond the traditional focus on sales and customer service. Hence, going forward, we expect to see a more personalised customer journey and greater use of data analytics to usher in the new era of retail banking worldwide.
References :
SHARE
RELATED BLOGS
January 16, 2023
Industry Trends on Retail Banking
The pandemic has accelerated changes in the retail banking industry, reshaping the competitive landscape, disrupting traditional banking areas such as branch banking and speeding up digitalisation. Lenders are adopting new strategies to overcome current challenges such as the low interest rate regime, the slow response time to new loan applications, high IT investment costs and regulatory compliance requirements. The global banking industry will continue to be buffeted by challenging macroeconomic headwinds in 2023, and a keen focus on resilience and innovation from retail banks is anticipated in response.
With a continuing emphasis on customer centricity, banks will augment digital capabilities and leverage data analytics to deliver hyper-personalized services and superior CX. Exploration of partnership and M&A opportunities with FinTechs may be on the rise; and cybersecurity, regulatory developments, and sustainability concerns will all play a role in strategy and decision-making as the retail banking sector moves forward.
Continued rise of fintech lenders
Legacy banks’ slow and cumbersome loan processing setups are being surpassed by the faster and easier online processes of fintech lenders or “neo-banks”. The rise of such “digitalonly” lenders is fuelled by a combination of megabanks’ bureaucratic processes, physical customer interaction not being required, due to the pandemic, and the easy availability of IT infrastructure. This has reduced the number of barriers to entry in the retail lending industry. A related study found fintech banks 1.8x more able to innovate and offer personalised products than legacy banks. Allied Market Research expects the global fintech lending market to grow at a CAGR of 27.4% from 2021 to 2030 to reach USD4.95tn by 2030. Fintech lenders’ digital-only presence keeps operating costs low, while innovative marketing techniques enable low-cost customer acquisition. For instance, fintech lenders have targeted niche groups that were underserved by big banks’ “one-size-fits-all” marketing strategy. Paybby, Fortú and Cheese are fintech startups that cater to the Black, Latino and Asian-American communities, respectively. Niche online lenders also exist to solely finance, say, automobile purchases or education loans. Specialised financing products such as “Buy Now, Pay Later” have gained sufficient popularity to replace credit cards and personal loans. These lenders may offer additional services such as insurance, payments and checking accounts to further encroach on legacy bank territory.
Data-driven credit risk models
Credit scoring models relying only on credit bureau-generated scores, such as FICO scores or VantageScores, are being supplemented with data analytics models to provide reliable alternative scoring systems. Credit decisions are increasingly based on behavioural data (spending patterns, bank account patterns, psychometric test data), personal data (such as level of education, occupation and number of years in current job) and income and asset data (such as stability of cash flow, property ownership and timely payment of rent and utility bills). Thus, thousands of data points can track not just the borrower’s ability to pay, but also their willingness and discipline to settle debts. For example, ZestFinance is a machine learning, risk-predicting platform that auto lenders (including Ford Motor) have used to reduce losses considerably. Traditional credit bureaus such as FICO and Experian have started offering credit scoring models (e.g., UltraFICO and Experian Boost) that incorporate a borrower’s transactional information using utility, telephone and television bill payment data, along with their deposit account information. Individuals denied access to lending products due to a lack of conventional credit scores may be able to avail themselves of credit based on these data-driven scoring models. However, concerns over the privacy of a consumer’s personal information may be an impediment for such models.
Greater use of technology
The biggest cost savings banks can make are in using process digitisation and automation to replace inefficient and manual processes in middle and back offices, according to KPMG. Banks in North America alone could save over USD70bn up to 2025 by leveraging technology. Technology was initially only meant to replace repeatable, “high volume, low value” tasks. However, artificial intelligence (AI), machine learning (ML) and automation are adding extra value at every step of the lending process. Some common examples are personalized AI chatbots, ML-enabled tools for KYC and data analytics to assist in loan pricing.
Conclusion
It is imperative for retail banks all over the world to maintain pace with the latest developments in the banking world. Especially in the time of WFH, there is a tendency for retail customers to choose lenders that go beyond the traditional focus on sales and customer service. Hence, going forward, we expect to see a more personalised customer journey and greater use of data analytics to usher in the new era of retail banking worldwide.
References :
Blogs
Industry Trends on Retail Banking
The pandemic has accelerated changes in the retail banking industry, reshaping the competitive landscape, disrupting traditional banking areas such as branch banking and speeding up digitalisation. Lenders are adopting new strategies to overcome current challenges such as the low interest rate regime, the slow response time to new loan applications, high IT investment costs and regulatory compliance requirements. The global banking industry will continue to be buffeted by challenging macroeconomic headwinds in 2023, and a keen focus on resilience and innovation from retail banks is anticipated in response.
With a continuing emphasis on customer centricity, banks will augment digital capabilities and leverage data analytics to deliver hyper-personalized services and superior CX. Exploration of partnership and M&A opportunities with FinTechs may be on the rise; and cybersecurity, regulatory developments, and sustainability concerns will all play a role in strategy and decision-making as the retail banking sector moves forward.
Continued rise of fintech lenders
Legacy banks’ slow and cumbersome loan processing setups are being surpassed by the faster and easier online processes of fintech lenders or “neo-banks”. The rise of such “digitalonly” lenders is fuelled by a combination of megabanks’ bureaucratic processes, physical customer interaction not being required, due to the pandemic, and the easy availability of IT infrastructure. This has reduced the number of barriers to entry in the retail lending industry. A related study found fintech banks 1.8x more able to innovate and offer personalised products than legacy banks. Allied Market Research expects the global fintech lending market to grow at a CAGR of 27.4% from 2021 to 2030 to reach USD4.95tn by 2030. Fintech lenders’ digital-only presence keeps operating costs low, while innovative marketing techniques enable low-cost customer acquisition. For instance, fintech lenders have targeted niche groups that were underserved by big banks’ “one-size-fits-all” marketing strategy. Paybby, Fortú and Cheese are fintech startups that cater to the Black, Latino and Asian-American communities, respectively. Niche online lenders also exist to solely finance, say, automobile purchases or education loans. Specialised financing products such as “Buy Now, Pay Later” have gained sufficient popularity to replace credit cards and personal loans. These lenders may offer additional services such as insurance, payments and checking accounts to further encroach on legacy bank territory.
Data-driven credit risk models
Credit scoring models relying only on credit bureau-generated scores, such as FICO scores or VantageScores, are being supplemented with data analytics models to provide reliable alternative scoring systems. Credit decisions are increasingly based on behavioural data (spending patterns, bank account patterns, psychometric test data), personal data (such as level of education, occupation and number of years in current job) and income and asset data (such as stability of cash flow, property ownership and timely payment of rent and utility bills). Thus, thousands of data points can track not just the borrower’s ability to pay, but also their willingness and discipline to settle debts. For example, ZestFinance is a machine learning, risk-predicting platform that auto lenders (including Ford Motor) have used to reduce losses considerably. Traditional credit bureaus such as FICO and Experian have started offering credit scoring models (e.g., UltraFICO and Experian Boost) that incorporate a borrower’s transactional information using utility, telephone and television bill payment data, along with their deposit account information. Individuals denied access to lending products due to a lack of conventional credit scores may be able to avail themselves of credit based on these data-driven scoring models. However, concerns over the privacy of a consumer’s personal information may be an impediment for such models.
Greater use of technology
The biggest cost savings banks can make are in using process digitisation and automation to replace inefficient and manual processes in middle and back offices, according to KPMG. Banks in North America alone could save over USD70bn up to 2025 by leveraging technology. Technology was initially only meant to replace repeatable, “high volume, low value” tasks. However, artificial intelligence (AI), machine learning (ML) and automation are adding extra value at every step of the lending process. Some common examples are personalized AI chatbots, ML-enabled tools for KYC and data analytics to assist in loan pricing.
Conclusion
It is imperative for retail banks all over the world to maintain pace with the latest developments in the banking world. Especially in the time of WFH, there is a tendency for retail customers to choose lenders that go beyond the traditional focus on sales and customer service. Hence, going forward, we expect to see a more personalised customer journey and greater use of data analytics to usher in the new era of retail banking worldwide.
References :
Founded in 1997, Optimum is a full-spectrum IT services and solutions company whose mission is to “Empower Business with Technology.”
© Optimum Solutions (S) Pte. Ltd.
Kindly fill in the form and we shall get back to you.
Kindly fill in the form and we shall get back to you.
