Artificial intelligence is among the most advanced technologies that are making their way through traditional methods of doing business across various industries. Especially in the banking sector, the adoption of high-tech tools integrated with artificial intelligence and business intelligence technologies is on the upward spiral. Mainly due to growing awareness about the benefits of business intelligence, the banking sector across the globe is expected to witness a sea change in the way various business operations have traditionally taken place.
This article sheds light on how the banking sector is rapidly advancing towards operational analytics through Big Data analytics, to ensure the highest levels of business integrity and cyber security. The banking sector is soon likely to undergo a widespread technological transformation by resorting to predictive and prescriptive analytics tools in the future. Read more to find important insights on how the future will unfold for the banking sector through a wide range of applications of tools integrated with predictive and prescriptive analytics.
Banking & Finance: Shift from Artificial Intelligence to Predictive Analytics
While business intelligence and artificial intelligence have found their way into bank accounts across the finance industry, stakeholders are constantly on the lookout for more advanced tools to fortify their businesses.
With the advent of such advanced technological tools, banks and other financial institutions are taking new steps towards achieving their goal of predicting the future trends in the banking and finance sector.
Predictive analytics is the latest technology that combines artificial intelligence and data mining technologies to process and analyze massive amounts of data and information. By acquiring relevant information and processing the data, various computer models integrated in predictive analytics tools can help businesses more accurately predict and prepare themselves for future events.
With the increasing adoption of predictive analytics tools, the banking sector is also witnessing a significant rise in the adoption of prescriptive analytics tools. While predictive analytics helps banking institutions understand and predict the future prospects of growth, prescriptive analytics enables them to fathom what actions need to be taken when facing the predicted future situations.
The banking & finance sector is leveraging the power of predictive analytics in predicting future trends vis-à-vis the economy and customer behaviours, and of prescriptive analytics for modifying business strategies based on those predictions.
Thereby, this indicates that banking businesses are heavily relying on business intelligence and data mining to gain insights on how management teams must alter their plan of action to get the expected results to sync with the changes that may influence the industry’s growth in the foreseeable future.
10 Top Applications of Predictive and Prescriptive Analytics in Banking and Finance Sector
1. Financial Management
Banks are heavily relying on business intelligence solutions for financial management today, in order to achieve substantial benefits of deploying currently available predictive and prescriptive analytics tools such as machine learning applications and big data analytics. Various business intelligence solutions are integrated with these technologies that can enable banking institutions to make data-driven decisions, especially while devising important business strategies vis-à-vis financial management.
2. Fraud Prevention
Fraud prevention is one of the most critical and sensitive applications of predictive and prescriptive analytics tools in the banking sector, mainly due to the rise of cyber attacks and cybersecurity threats. Banks that are equipped with advanced tools with predictive and prescriptive analytics can easily identify such problems. This can ultimately help them accurately predict the potential threats and prevent the breach of sensitive data and potential for fraud.
3. Application Screening
Banking institutions and many such financial bodies are often flooded with innumerable applications. In the modern, technological era, banks are making use of predictive and prescriptive analytics tools to process and analyze massive volumes of applications without any delays or errors.
4. Better Liquidity/Cash Planning
Banks are using predictive and prescriptive analytics, not only in financial management applications, but also in ensuring better planning for liquidity or availability of cash. As optimal management of liquid assets through business intelligence can lead to more profitable outcomes, predictive and prescriptive analytics tools are likely to gain more popularity in the finance sector.
5. Customer Acquisition and Retention
Predictive and prescriptive analytics tools make use of state-of-the-art, data-driven technologies, such as Artificial Intelligence, machine learning, and Big Data, to identify opportunities for customer acquisition and retention. Banks can leverage business intelligence to run campaigns for customer retention and new customer acquisition with more optimized targeting, which can help them easily spot high-value customer segments that are more likely to respond to these campaigns.
6. Knowing Customer Buying Habits
Banking institutions and financial organizations, especially the ones that operate independently, usually have to grapple with the problems associated with introducing the right scheme for their target customers. Now, with the use of AI-driven predictive and prescriptive analytics tools, the entire banking sector can easily identify the potential changes in the behaviour of their target customer group. This can ultimately result in more successful products and schemes, and thereby, the positive growth of the banking sector.
7. Loan Approval
Banks are getting more sophisticated with the system they follow to evaluate loan applications. Taking into consideration the fact that some applications can be approved despite the applicants not having a high FICO score, banking and financial institutions are relying on advanced predictive and prescriptive analytics tools to reduce unnecessary denial of loans, helping non-traditional borrowers get their loans approved.
8. Cross-selling
By analyzing the behaviours and purchasing trends of their existing customers, banking and financial organizations are trying to spot opportunities where multiple products and schemes can be pitched. Predictive and prescriptive analytics tools can help these financial bodies to identify the potential for successful and efficient cross-selling. This not only contributes to the profitability of the organization, but also helps in improving customer relationships, opening better opportunities in the future.
9. Customer Lifetime Value (LTV)
Banking institutions, today, are struggling to ensure that their customers do not move to better schemes offered by their competitors, as the intensity of competition is only increasing with time. With the help of predictive and prescriptive analytics tools, banking organizations are streamlining their customer engagement efforts, in order to achieve some wins based on customer lifetime value.
10. Customer Relationship Management
This is one of the most practical applications of predictive and prescriptive analytics tools, as they can precisely notify banks about how their customer relationship management should be modified or improved, with the help of various insights such as the list of customers that the banks must focus on with better customer engagement efforts, how customers have responded to certain promotions in the past, and how to get better returns on various customer engagement activities.