Machine Learning Software Reduces Time to Map Competencies to ...
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Generated: 10/11/2021
Machine Learning Software Reduces Time to Map Competencies to ...
(Nanowerk 2015)
Machine learning technology has revolutionized the financial services sector.
For some time now, financial advisors must manually enter hundreds of pieces of information
to generate financial reports and track performance of portfolios. But a team-based process
approach can provide a significant increase in productivity through the use of ML-based
tools.
The advent of the technology that is able to learn its own way and adapt in a fast-growing
field is promising. Now, financial institutions are investing into such technology, in order
to replace the costly and tedious manual processes and increase the productivity of their
customer facing operations.
In this white paper, we will explore how ML software has the potential to decrease the
time to map each competency to a specific skill in the financial services industry. There are
several examples of specific use cases of the application of such tools but there is only one
company that has used an ML approach to map competencies to skills to train new consultants.
The software is called 'Predictive Intelligence' and uses a new, patented approach to build
intelligent decision support systems. In contrast to most ML technology, the technology allows
for an approach to ML, where an ML system is able to learn through itself, by gathering expert
recommendations. The system automatically generates its own decisions and learns through
training examples from experts and the company's own knowledge.
A large, global financial services company has used this technology to map competencies
to skills for a competency-based induction program. The competency mapping project showed how
Predictive Intelligence software could be used in a new way: To teach the company's new
consultants about each of the core competencies in the areas selected for the program.
In the past, such ML-based solutions could only be used as a reporting tool. In this case study,
Predictive Intelligence was developed to actually build competency trees and competency maps
to train new consultants. So far, the results are positive, and the technology allowed a company
in the financial services sector spend only 2-3 days to map the competencies to the skills of
its consultants.
ML software for Financial Services: More Than Machine Learning
At the core of all modern ML techniques stands the process of collecting, organizing and
evaluating data. The challenge, however, lies in collecting sufficient (and relevant) data.
This is where the idea of ML starts to get interesting -- by collecting and evaluating the right
data you can begin to build powerful mathematical models in a very short amount of time. Once
the data has been evaluated, these models can be run several times to predict what will happen
next based on a new input.
In a nutshell, the data you collect for an ML solution is called the feature set.
But, what data to collect in order to solve your industry’s problems? To identify opportunities,
one must understand what type of problem you have.
ML software in Finance can be helpful in three areas:
Inventory Management: Identifying new products to be included in new or pending products.
Predictive Analytics: Identifying and predicting the impact of certain events or trends.
Portfolios and Portfolio Analysis: Identifying opportunities for higher returns and risks.
How can machine learning support the finance industry?
Predictive Analyzer: When it comes to ML, all software requires data. There is no way around it.
But, ML is no silver bullet. To effectively use ML solutions, it is necessary to know exactly
what type of data you should be collecting. In other words, what are the data sources and how
often should they be accessed?
A large number of ML tools are available to the financial services industry. Some companies
have developed their own solution, while others are developing their proprietary solutions with
their ML technology partners. However, an interesting trend is to have tools that work
together to provide the best value and experience.
There are many tools available, both cloud based and on-premises. Some of the available
ML technology includes:
The need for companies to collect their own data has been a driving factor with the use of
ML by financial institutions. However, there is an additional side of the equation: a company's
ability to provide the data. If you have a data set, it is necessary to ensure the data is
reliable, relevant and accurate.
Machine Learning in FinTech: A Paradigm Shifting Shift
Machine learning and Artificial Intelligence (AI) is the current hot topic in Finance.
It’s not surprising, as the Financial Services Industry faces challenges today with the
rapid growing of new services and digital channels.
FinTech companies are currently developing solutions that are able to create innovative
products and services, and at the same time, reduce high operational costs.
To do that, they are turning to machine learning, using AI and technology-enabled analysis to
provide the best results.
As a growing sector, FinTech is a highly relevant market niche for many companies to start
exploring.
But they will need to start from scratch or work with technology that is already out there.
Which technologies are the most promising in machine learning and FinTech?
With the emergence of FinTech and the advent of the financial services industry, FinTech companies
are in search of solutions to help them improve the entire process, which is becoming more and
more complex. This is driving the need for the new FinTech market to explore the possibilities
of different technologies to solve it:
Finance professionals, along with financial institutions in general, need to ensure that the quality
of data collection and information gathering is done correctly to ensure the most relevant data is
used. For machine learning, data collection is a critical element.
But, data collection and quality should not be a limiting factor. To effectively collect data and have
a scalable solution, there is a need for new technologies to build data models.
How Will Finance Professionals Benefit from ML and AI?
Machine learning and AI are rapidly evolving the world around us. With the introduction of new
technologies, machine learning can be used to solve current problems, create innovative products,
and drive new services.
Finance professionals can use the technology to improve their core activities:
Automate processes to increase efficiency and improve performance
Better understand business requirements to build the right products
Instrument more complex situations with decision trees, fuzzy logic and other ML models
Improve data quality and accuracy and reduce costs and risks
Finance has faced challenges in data quality and accuracy. Financial professionals and institutions
currently depend on manual processes and tedious Excel data entry to produce business reports.
Yet, while this process is accurate it lacks in efficiency. This leaves financial firms missing
opportunities. The result? A more complex process to monitor, control and analyze the financial
performance. Machine learning and AI can be powerful tools to improve efficiency and mitigate
risk.
Why Machine learning and AI is an exciting opportunity in Finance
Technology continues to evolve and so do data science and machine learning. This is a very
interesting development, as it’s the ability to adapt to fast changing situations. FinTech and
machine learning are the perfect candidates to leverage technology. Because both are relatively
young and developing, this is an exciting opportunity for both industries and FinTech companies.
Financial institutions stand to benefit by leveraging machine learning technologies to improve
the process of identifying new solutions to problems and new products to be put into the hands
of consumers or business owners. Machine learning is the underlying technology of these solutions.
The application of machine learning to Finance
Financial firms are looking for innovative solutions that allow them to create competitive products
and services, and improve the overall customer experience. These solutions can be enabled using
an AI solution such as machine learning.
The challenge, however, is in finding the best type of solution for specific use cases
In order to be a real disruptive technology, Machine Learning (ML) solutions must be able to be
implemented in three core areas:
Finance:
Customer Service:
Human Centricity
Data Collection:
To be a real asset in the Finance industry, ML solutions must be able to support all three of
these areas.
The three ways to leverage data in Finance:
Customer Intelligence: The ultimate goal of financial institutions is to improve the performance
of a customer with the use of data. To do that, they are looking to innovate and build new products
and services.
But in order to do that, they need to be able to define a solution based on a customer journey.
This involves a very important element: the ability to gather information from the customer in
the best way possible. From the first time a financial services person interacts with a customer to
the time when the final transaction is processed.
This information can be extremely diverse: from phone calls to emails, from chats to live on-site
customers, from the use of web pages or mobile devices -- to name a few.
Customer behavior is all over the map but it is a very relevant data to collect in order to get
a complete picture of a customer's interaction.
(Nanowerk 2015)
Machine learning technology has revolutionized the financial services sector.
For some time now, financial advisors must manually enter hundreds of pieces of information
to generate financial reports and track performance of portfolios. But a team-based process
approach can provide a significant increase in productivity through the use of ML-based
tools.
The advent of the technology that is able to learn its own way and adapt in a fast-growing
field is promising. Now, financial institutions are investing into such technology, in order
to replace the costly and tedious manual processes and increase the productivity of their
customer facing operations.
In this white paper, we will explore how ML software has the potential to decrease the
time to map each competency to a specific skill in the financial services industry. There are
several examples of specific use cases of the application of such tools but there is only one
company that has used an ML approach to map competencies to skills to train new consultants.
The software is called 'Predictive Intelligence' and uses a new, patented approach to build
intelligent decision support systems. In contrast to most ML technology, the technology allows
for an approach to ML, where an ML system is able to learn through itself, by gathering expert
recommendations. The system automatically generates its own decisions and learns through
training examples from experts and the company's own knowledge.
A large, global financial services company has used this technology to map competencies
to skills for a competency-based induction program. The competency mapping project showed how
Predictive Intelligence software could be used in a new way: To teach the company's new
consultants about each of the core competencies in the areas selected for the program.
In the past, such ML-based solutions could only be used as a reporting tool. In this case study,
Predictive Intelligence was developed to actually build competency trees and competency maps
to train new consultants. So far, the results are positive, and the technology allowed a company
in the financial services sector spend only 2-3 days to map the competencies to the skills of
its consultants.
ML software for Financial Services: More Than Machine Learning
At the core of all modern ML techniques stands the process of collecting, organizing and
evaluating data. The challenge, however, lies in collecting sufficient (and relevant) data.
This is where the idea of ML starts to get interesting -- by collecting and evaluating the right
data you can begin to build powerful mathematical models in a very short amount of time. Once
the data has been evaluated, these models can be run several times to predict what will happen
next based on a new input.
In a nutshell, the data you collect for an ML solution is called the feature set.
But, what data to collect in order to solve your industry’s problems? To identify opportunities,
one must understand what type of problem you have.
ML software in Finance can be helpful in three areas:
Inventory Management: Identifying new products to be included in new or pending products.
Predictive Analytics: Identifying and predicting the impact of certain events or trends.
Portfolios and Portfolio Analysis: Identifying opportunities for higher returns and risks.
How can machine learning support the finance industry?
Predictive Analyzer: When it comes to ML, all software requires data. There is no way around it.
But, ML is no silver bullet. To effectively use ML solutions, it is necessary to know exactly
what type of data you should be collecting. In other words, what are the data sources and how
often should they be accessed?
A large number of ML tools are available to the financial services industry. Some companies
have developed their own solution, while others are developing their proprietary solutions with
their ML technology partners. However, an interesting trend is to have tools that work
together to provide the best value and experience.
There are many tools available, both cloud based and on-premises. Some of the available
ML technology includes:
The need for companies to collect their own data has been a driving factor with the use of
ML by financial institutions. However, there is an additional side of the equation: a company's
ability to provide the data. If you have a data set, it is necessary to ensure the data is
reliable, relevant and accurate.
Machine Learning in FinTech: A Paradigm Shifting Shift
Machine learning and Artificial Intelligence (AI) is the current hot topic in Finance.
It’s not surprising, as the Financial Services Industry faces challenges today with the
rapid growing of new services and digital channels.
FinTech companies are currently developing solutions that are able to create innovative
products and services, and at the same time, reduce high operational costs.
To do that, they are turning to machine learning, using AI and technology-enabled analysis to
provide the best results.
As a growing sector, FinTech is a highly relevant market niche for many companies to start
exploring.
But they will need to start from scratch or work with technology that is already out there.
Which technologies are the most promising in machine learning and FinTech?
With the emergence of FinTech and the advent of the financial services industry, FinTech companies
are in search of solutions to help them improve the entire process, which is becoming more and
more complex. This is driving the need for the new FinTech market to explore the possibilities
of different technologies to solve it:
Finance professionals, along with financial institutions in general, need to ensure that the quality
of data collection and information gathering is done correctly to ensure the most relevant data is
used. For machine learning, data collection is a critical element.
But, data collection and quality should not be a limiting factor. To effectively collect data and have
a scalable solution, there is a need for new technologies to build data models.
How Will Finance Professionals Benefit from ML and AI?
Machine learning and AI are rapidly evolving the world around us. With the introduction of new
technologies, machine learning can be used to solve current problems, create innovative products,
and drive new services.
Finance professionals can use the technology to improve their core activities:
Automate processes to increase efficiency and improve performance
Better understand business requirements to build the right products
Instrument more complex situations with decision trees, fuzzy logic and other ML models
Improve data quality and accuracy and reduce costs and risks
Finance has faced challenges in data quality and accuracy. Financial professionals and institutions
currently depend on manual processes and tedious Excel data entry to produce business reports.
Yet, while this process is accurate it lacks in efficiency. This leaves financial firms missing
opportunities. The result? A more complex process to monitor, control and analyze the financial
performance. Machine learning and AI can be powerful tools to improve efficiency and mitigate
risk.
Why Machine learning and AI is an exciting opportunity in Finance
Technology continues to evolve and so do data science and machine learning. This is a very
interesting development, as it’s the ability to adapt to fast changing situations. FinTech and
machine learning are the perfect candidates to leverage technology. Because both are relatively
young and developing, this is an exciting opportunity for both industries and FinTech companies.
Financial institutions stand to benefit by leveraging machine learning technologies to improve
the process of identifying new solutions to problems and new products to be put into the hands
of consumers or business owners. Machine learning is the underlying technology of these solutions.
The application of machine learning to Finance
Financial firms are looking for innovative solutions that allow them to create competitive products
and services, and improve the overall customer experience. These solutions can be enabled using
an AI solution such as machine learning.
The challenge, however, is in finding the best type of solution for specific use cases
In order to be a real disruptive technology, Machine Learning (ML) solutions must be able to be
implemented in three core areas:
Finance:
Customer Service:
Human Centricity
Data Collection:
To be a real asset in the Finance industry, ML solutions must be able to support all three of
these areas.
The three ways to leverage data in Finance:
Customer Intelligence: The ultimate goal of financial institutions is to improve the performance
of a customer with the use of data. To do that, they are looking to innovate and build new products
and services.
But in order to do that, they need to be able to define a solution based on a customer journey.
This involves a very important element: the ability to gather information from the customer in
the best way possible. From the first time a financial services person interacts with a customer to
the time when the final transaction is processed.
This information can be extremely diverse: from phone calls to emails, from chats to live on-site
customers, from the use of web pages or mobile devices -- to name a few.
Customer behavior is all over the map but it is a very relevant data to collect in order to get
a complete picture of a customer's interaction.