TECHNICAL FIELD

This invention relates to an Artificial Intelligence (Al) and database enabled risk management system designed to assist individuals, businesses, NGOs, and vulnerable communities. In particular, this invention relates to a risk management system for creating and reporting a risk assessment of a query to a user together with a set of mitigation options that would benefit the user.

SUMMARY OF THE INVENTION

According to the invention, there is provided a risk management system including: - a receiving means for receiving a query from a user; a searching means for searching for and retrieving data relating to the query; an assignment means for assigning predefined risk parameters to the query based on a subject of the query; a filtering means for filtering the retrieved data based on the assigned risk parameters in order to isolate the data most relevant to the risk parameters; a weighting means for weighting the filtered data; and a calculating means for calculating a risk score based on the weighted data.

A reporting means may be provided for reporting the risk score to the user.

A registration means may be provided for allowing the user to register its details and to create a user profile to enable the user to utilise the risk management system. The registration means may be configured to store the user details on the user profile. The user profile may be stored on a user database. The user details may include information related to any one or more of the group including: a name, a username, a user code, a password, biometric identification, a unique number combination, an identification number, and the like.

A verification means may be provided for verifying the details of the user. The verification means may be configured to compare the identification details of the user with identification details on an identification database. The identification database may form part of a private, personal, public and/or official database. Private databases may be in the form of financial institution databases, customer databases, private social media accounts, and the like. Personal databases may be in the form of a personal data storage device having permanent and/or temporary memory, such as smart phones, laptops, and the like. Public databases may include public social media accounts, telephone directories, and the like. Official databases may be in the form of verified official government databases such as law enforcement databases, health service databases, government run databases relating to the identity and status of citizens, and the like.

The query may be in the form of any query that may be generally searched using a search engine. The subject of the query may relate to any one of the group including, but not limited to, a location, a natural person, a juristic person or organisation, a tangible object, an intangible object, and an activity.

Queries related to a location may include any one or more of the group including, but not limited to, a road name, building name, housing complex name, street address, a highway, an intersection, a highway onramp, a highway offramp, a village, a town, a city, a country, a province, a state, a country, a continent, a border post, a region, a location name, a set of coordinates, a place nickname, and the like.

Queries related to a natural person may include any primary identifiers of the group including name, title, biometric information, and the like. The biographic information may include facial recognition images, behavioural characteristics, fingerprints, DNA, extremist social media posts and the like. Queries relating to a natural person may include any secondary identifiers of the group including residential address, identity number, social security number, birth date, occupation, extremist political or hate group membership listings, resume and the like. Preferably, the primary and/or secondary identifiers may be publicly available.

An input means may be provided for allowing the user to input the query to be received by the receiving means. The input means may be remotely operable wherein the user is able to use a dedicated application installed on a device or a webpage accessible by an internet browser. The device may be capable of storing and running application software. The device may be connectable to the internet. Preferably, the device may be in the form of a smart device. The smart device may be in the form of any one or more of the group including: a smartphone, a smartwatch, a laptop, a desktop, a tablet, and the like. The user may input the query into the input means by way of typing, voice, image, copying and pasting, or any other suitable method. The query may be stored in a storage component of the device. The query may be permanently or temporarily stored on the storage.

The receiving means may be arranged in communication with the input means. The receiving means may be configured to receive information from the input means via any suitable method including, but not limited to, wire connections, radio, Bluetooth, Wi-Fi, infrared, cellular network, USSD, 3G, 4G, satellite communication, API links to other databases, and the like.

The searching means may be arranged in communication with the receiving means for permitting reading of the query by the searching means in order to allow the query to be searched. The searching means may have access to a plurality of data sources such as public databases, official databases, websites, repositories, and the like. The searching means may be in the form of data scraping, web scraping, API (Application Programming Interface) scraping, keyword usage searching, image searching, and the like. The searching means may be in the form of a search engine or a plurality of search engines. The searching means may be configured to store the retrieved data in a result database. The result database may have proprietary API’s for accessing the data.

The result database may be in the form of a centralised database, a cloud database, a commercial database, distributed database, a structured database (SQL), a non-structured database (NoSQL), an object-oriented database, an open-source database, an operational database, and the like. The result database may be in the form of any data storage device of the group including hard disk drive, SSD (solid state drive), server, network attached server, peer-to-peer network, cloud-based storage, and the like. Preferably, the result database may be in the form of a highly secured cloud database. The result database may store information in a predefined structure based on the subject of the query, preferably to support further training of the Al module.

A second input means may be provided for allowing the user to input data relating to any subject for storage on a user populated database. The user populated database may be searchable by the searching means.

A validation means may be provided for validating information retrieved by the searching means and/or data provided by the user. The information may be validated, preferably primarily, through an Al (artificial intelligence) module, capable of Machine Learning. Preferably, only validated information may be stored on the populated and/or result database. In an alternative form of the invention, a human implemented validation means may be added to the system when deployed on a contract basis. The database may purge unvalidated information and may send out rescission notices to users if these largely formed the basis of an issued result or warning. The system's algorithms may aim for a low tolerance threshold for rescissions and may limit the use of sources linked to multiple rescissions. The assignment means may assign predefined risk parameters to the query based on the subject of the query.

The predefined risk parameters in instances where the query is related to a location may be in the form of any one or more of the group including travel time, road closures, traffic, incidents in the area, business hours, directions, itineraries, distance in relation to important facilities such as airports, hospitals, and schools, status of the place, news and current developments, crime statistics, policing statistics, extremist or hate group activity, accommodation, dining, entertainment, historical relevance, specific laws or common practice, accessibility, access to necessities, travel guides, health and safety aspects, public and private transportation, visa and/or other travel document/legal requirements, weather and/or climate, average temperature, rainfall, natural disasters, pollution, international relations, social issues such as homelessness, political information such as political instability, war, demographic information such as race, language used, disease outbreaks, and the like.

The predefined risk parameters in instances where the query is related to a natural person may be in the form of any one or more of the group including publicly available law enforcement records such as criminal records, sex offender lists and the like; demographic information such as age, education, employment status, and the like.

The predefined risk parameters in instances where the query is related to a natural person may be in the form of any one or more of the group including private information such as medical records, health insurance records, and the like; financial institution records such as credit history, credit scores, blacklisting, information relating to accounts and transactions, and the like.

The predefined risk parameters in instances where the query is related to a juristic person may be in the form of any one or more of the group including place of business, business structure such as corporation, sole proprietorship and the like, intellectual property ownership, previous business dealings, confirmable direct or third party relationships with high risk entities, extremist political groups and persons accused or convicted of illegality, insurance, recruitment, links to other organisations, awards and accolades, reviews, blockchain, financial institution records such as financial status of the business, credit history, credit scores, blacklisting, information relating to accounts and transactions, and the like, community involvement, accreditation, legal status, employees, and the like.

The predefined risk parameters in instances where the query is related to a tangible object may be in the form of any one or more of the group including price, health effects, side effects of use, usability, accessibility, availability, weight, mass, capacity, volume, size, impact on the environment, current value, resale value, storage instructions, use instructions, warnings associated with the object, security, lifespan of the object, disposal methods, and the like.

The predefined risk parameters in instances where the query is related to an intangible object may be in the form of any one or more of the group including price, value, resale value, security, lifespan of the object, disposal, accessibility, availability, usability, effects of use, reputation, and the like.

The predefined risk parameters in instances where the query is related to an activity may be in the form of any one or more of the group including price, health effects, side effects of engaging in the activity, accessibility, availability, security, value, impact on the environment, safety warnings associated with the activity, risk of default, risk of delay, risk of non-payment, third party risk, and the like.

The predefined risk parameters may be arranged in order of varying factors of the group including but not limited to type of risk, impact of risk, severity of risk, probability of risk, volatility of risk, geographic scale of risk, and the like. The filtering means may be provided for filtering the retrieved data based on the assigned risk parameters in order to isolate the data most relevant to the risk parameters. The filtering means may be in the form of an Al algorithm, Al module, or any other suitable programmable means.

The weighting means may be provided for weighting the filtered data. Preferably, weightings of the filtered data may be pre-defined and may be query specific. The weighting means may be in the form of an Al algorithm, Al module, or any other suitable programmable means. Preferably, the data may be weighted based on the impact and probability associated with the risk of the subject of the query. The weighting means may weight the data into predefined numerical categories of varying risk. The weighting means may weight the data into predefined categories represented by text values on a scale, such as no risk, minor risk, moderate risk, major risk, and severe risk.

The calculating means may be provided for calculating the risk score based on the weighted data. The calculating means may be in the form of an Al technology such as an Al algorithm, Al module, or any other suitable programmable means. The calculating means may be capable of machine learning or deep learning. The calculating means may use the weighted data to calculate the risk score using a matrix or any other suitable means of calculating the risk score.

The risk score may be in the form of any one or more of the group including a numerical value, a numerical scale, a percentage, a visual representation such as a graph, a colour scheme, an image, a chart, a map, a visual scale, and the like, a text value describing the risk score such as no risk, minor risk, moderate risk, major risk, severe risk, and the like. A display device may be provided for displaying the risk score to the user. The display device may be arranged in communication with any one or more of the registration means, the verification means, the input means, the receiving means, the searching means, the user database, the identification database, the result database, the user-populated database, the validation means, the assignment means, the filtering means, the weighting means, and the calculating means.

The reporting means may be configured to create a risk report which may be displayed on the display device. The risk report may be conveyed to the user by any suitable means such as text, Al voice generator, images, links, graphs, summaries, charts, maps, infographics, and the like. The risk report may include information such as the risk score, potential and/or beneficial risk-mitigating factors and strategies, links to databases or websites storing information related to the risk, tips and/or suggestions, alternatives, and the like.

BRIEF DESCRIPTION OF THE DRAWINGS

A risk management system in accordance with the invention will now be described by way of the following, non-limiting examples with reference to the accompanying drawings.

In the drawings: -

Figure 1 is a schematic diagram showing the general functioning of the risk management system in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Referring now to the Figures, reference numeral 10 refers generally to a risk management system in association with the present invention. The risk management system 10 includes a receiving means 12 for receiving a query 14 from a user 16, a searching means 18 for searching for and retrieving data relating to the query 14, an assignment means 20 for assigning predefined risk parameters 22 to the query 14 based on a subject of the query 14, a filtering means 24 for filtering the retrieved data based on the assigned risk parameters in order to isolate the data most relevant to the risk parameters, a weighting means 26 for weighting the filtered data, and a calculating means 28 for calculating a risk score 30 based on the weighted data.

A reporting means 30 is provided for reporting the risk score 30 to the user 16.

A registration means 34 is provided for allowing the user 16 to register its details and to create a user profile 36 to enable the user 16 to utilise the risk management system 10. The registration means 34 is configured to store the user details on the user profile 36. The user profile 36 is stored on a user database 38.

The user 16 details includes information related to any one or more of the group including: a name, a username, a user code, a password, biometric identification, a unique number combination, an identification number, and the like.

A verification means 40 is provided for verifying the details of the user 16. The verification means 40 is configured to compare the identification details of the user with identification details on an identification database 42. The identification database 42 forms part of a private, personal, public and/or official database. Typically, private databases are in the form of financial institution databases, customer databases, private social media accounts, and the like. Typically, personal databases are in the form of a personal data storage device having permanent and/or temporary memory, such as smart phones, laptops, and the like. Typically, public databases include public social media accounts, telephone directories, and the like. Typically, official databases are in the form of verified official government databases such as law enforcement databases, health service databases, government run databases relating to the identity and status of citizens, and the like. The query 14 is in the form of any query that is generally searched using a search engine. The subject of the query 14 typically relates to any one of the group including a location, a natural person, a juristic person or organisation, a tangible object, an intangible object, and an activity.

Queries 14 related to a location include any one or more of the group including, but not limited to, a road name, building name, housing complex name, street address, a highway, an intersection, a highway onramp, a highway offramp, a village, a town, a city, a country, a province, a state, a country, a continent, a border post, a region, a location name, a set of coordinates, a place nickname, and the like.

Queries 14 related to a natural person include any primary identifiers of the group including name, biometric information, and the like. The biographic information may include facial recognition images, behavioural characteristics, fingerprints, DNA, extremist social media posts and the like. Queries 14 relating to a natural person include any secondary identifiers of the group including residential address, identity number, social security number, birth date, occupation, extremist political or hate group membership listings, resume and the like. Typically, the primary and secondary identifiers are publicly available.

An input means 44 is provided for allowing the user 16 to input the query 14 to be received by the receiving means 12. The input means 44 is remotely operable wherein the user 16 is able to use a dedicated application installed on a device 46 or a webpage accessible by an internet browser. The device 46 is capable of storing and running application software. The device 46 is connectable to the internet. Typically, the device 26 is in the form of a smart device. The smart device is in the form of any one or more of the group including a smartphone, a smartwatch, a laptop, a desktop, a tablet, and the like. The user 16 inputs the query 14 into the input means 44 by way of typing, voice, image, copying and pasting, or any other suitable method. The query 14 is stored in a storage component of the device 26. The query 14 is permanently or temporarily stored on the storage of the device 26. The receiving means 12 is arranged in communication with the input means 44. The receiving means 12 is configured to receive information from the input means 44 via any suitable method including, but not limited to, wire connections, radio, Bluetooth, Wi-Fi, infrared, cellular network, USSD, 3G, 4G, satellite communication, API links to other databases, and the like.

The searching means 18 is arranged in communication with the receiving means 12 for permitting reading of the query 14 by the searching means 18 in order to allow the query 14 to be searched. The searching means 18 is configured to access a plurality of data sources 48 such as public databases, official databases, websites, repositories, and the like. The searching means 18 is in the form of data scraping, web scraping, API (Application Programming Interface) scraping, and the like. The searching means 18 is in the form of a search engine or a plurality of search engines. The searching means 18 is configured to store the retrieved data in a result database 50. It is to be appreciated that the result database 50 has propriety API’s for accessing the data.

The result database 50 is in the form of a centralised database, a cloud database, a commercial database, distributed database, a structured database (SQL), a non-structured database (NoSQL), an object-oriented database, an open-source database, an operational database, and the like. The result database 50 is in the form of any data storage device of the group including hard disk drive, SSD (solid state drive), server, network attached server, peer-to-peer network, cloud-based storage, and the like. Typically, the result database 50 is in the form of a highly secured cloud database. The result database 50 stores information in a predefined structure based on the subject of the query 14 to support training of the Al module.

A second input means 52 is provided for allowing the user 16 to input data relating to any subject for storage on a user populated database 54. The user populated database 54 is searchable by the searching means 18. A validation means 56 is provided for validating information retrieved by the searching means 18 and/or data provided by the user 16. The information is primarily validated through an Al (artificial intelligence) module, capable of machine learning. Typically, only validated information is stored on the user populated database 56 and/or result database 50.

In an alternative form of the invention, a human implemented validation means is added to the system when deployed on a contract basis. The database purges unvalidated information and sends out rescission notices to users if these largely formed the basis of an issued result or warning. The system’s algorithms aim for a low tolerance threshold for rescissions and limit the use of sources linked to multiple rescissions.

The assignment means 20 assigns predefined risk parameters 22 to the query 16 based on the subject of the query 16.

The predefined risk parameters 22 in instances where the query 14 is related to a location are in the form of any one or more of the group including travel time, road closures, traffic, incidents in the area, business hours, directions, itineraries, distance in relation to important facilities such as airports, hospitals, and schools, status of the place, news and current developments, crime statistics, policing statistics, extremist or hate group activity, accommodation, dining, entertainment, historical relevance, specific laws or common practice, accessibility, access to necessities, travel guides, health and safety aspects, public and private transportation, visa and/or other travel document/legal requirements, weather and/or climate, average temperature, rainfall, natural disasters, pollution, international relations, social issues such as homelessness, political information such as political instability, war, demographic information such as race, language used, disease outbreaks, and the like. The predefined risk parameters 22 in instances where the query 14 is related to a natural person are in the form of any one or more of the group including publicly available law enforcement records such as criminal records, sex offender lists and the like; demographic information such as age, education, employment status, and the like.

The predefined risk parameters 22 in instances where the query 14 is related to a natural person are in the form of any one or more of the group including private information such as medical records, health insurance records, and the like; financial institution records such as credit history, credit scores, blacklisting, information relating to accounts and transactions, and the like

The predefined risk parameters 22 in instances where the query 14 is related to a juristic person or organisation are in the form of any one or more of the group including place of business, business structure such as corporation, sole proprietorship and the like, intellectual property ownership, previous business dealings, confirmable direct or third party relationships with high risk entities, extremist political groups and persons accused or convicted of illegality, insurance, recruitment, links to other organisations, awards and accolades, reviews, blockchain, financial institution records such as financial status of the business, credit history, credit scores, blacklisting, information relating to accounts and transactions, and the like, community involvement, accreditation, legal status, employees, and the like.

The predefined risk parameters 22 in instances where the query 16 is related to a tangible object are in the form of any one or more of the group including price, health effects, side effects of use, usability, accessibility, availability, weight, mass, capacity, volume, size, impact on the environment, current value, resale value, storage instructions, use instructions, warnings associated with the object, security, lifespan of the object, disposal methods, and the like. The predefined risk parameters 22 in instances where the query 16 is related to an intangible object are in the form of any one or more of the group including price, current value, resale value, security, lifespan of the object, disposal, accessibility, availability, usability, effects of use, reputation, and the like.

The predefined risk parameters 22 in instances where the query 16 is related to an activity are in the form of any one or more of the group including price, health effects, side effects of engaging in the activity, accessibility, availability, security, value, impact on the environment, safety warnings associated with the activity, risk of default, risk of delay, risk of non-payment, third party risk, and the like.

The predefined risk parameters 22 are arranged in order of varying factors including but not limited to type of risk, impact of risk, severity of risk, probability of risk, volatility of risk, geographic scale of risk, and the like.

The filtering means 24 is provided for filtering the retrieved data based on the assigned risk parameters 22 in order to isolate the data most relevant to the risk parameters 22. The filtering means 24 is in the form of an Al algorithm, Al module, or any other suitable programmable means.

The weighting means 26 is provided for weighting the filtered data. Typically, weightings of the filtered data are pre-defined and are query 14 specific. The weighting means 26 is in the form of an Al algorithm, Al module, or any other suitable programmable means. Typically, the data is weighted based on the impact and probability associated with the risk of the subject of the query 14. The weighting means 26 weighs the data into predefined numerical categories of varying risk. The weighting means 26 weighs the data into predefined categories represented by text values on a scale, such as no risk, minor risk, moderate risk, major risk, and severe risk. The calculating means 28 is in the form of an Al technology such as an Al algorithm, Al module, or any other suitable programmable means. The calculating means 28 is capable of machine learning or deep learning. The calculating means 28 uses the weighted data to calculate the risk score 30 using a matrix or any other suitable means of calculating the risk score 30.

The risk score 30 is in the form of any one or more of the group including a numerical value, a numerical scale, a percentage, a visual representation such as a graph, a colour scheme, an image, a chart, a map, a visual scale, and the like, a text value describing the risk score such as no risk, minor risk, moderate risk, major risk, severe risk, and the like.

A display device 58 is provided for displaying the risk score 30 to the user 16. The display device 58 is arranged in communication with any one or more of the registration means 34, the verification means 40, the input means 44, the receiving means 12, the searching means 18, the user database 38, the identification database 42, the result database 50, the user-populated database 54, the validation means 56, the assignment means 20, the filtering means 24, the weighting means 26, and the calculating means 28.

The reporting means 32 is provided for reporting the risk score 30 to the user 16. The reporting means 32 is configured to create a risk report 60 which is displayed on the display device 58. The risk report 60 is conveyed to the user by any suitable means such as text, Al voice generator, images, links, graphs, summaries, charts, maps, infographics, and the like. The risk report 60 includes information such as the risk score, potential and/or beneficial risk-mitigating factors and strategies, links to databases or websites storing information related to the risk, tips and/or suggestions, alternatives, and the like.

Example 1 : Where the subject of the query is a location The user 16 inputs the query 14: “Johannesburg” into the input means 44. The searching means 18 searches for and retrieves information relating to “Johannesburg” from a variety of data sources 48. The retrieved information is validated by the validation means 40 and stored on the result database 50. The data is then filtered by the filtering means 24 to isolate the data relevant to pre-defined risk parameters 22 relating to a location, in this case, specifically to a city, more specifically to the city of Johannesburg. These risk parameters 22 may be crime statistics, public transport, weather, and the like. The filtered data is then weighted by the weighting means 26 in terms of impact and probability associated with the risk of the subject of the query 14. The calculating means 28 calculates a risk score 30 based on the weighted data and a risk report 60 is made available to the user 16 via the display device 58. The risk score 12 and other information may form part of the risk report 60 allows the user to assess the risk of Johannesburg. The risk score 30 for Johannesburg may be represented as a numerical value such as "60%”, “6 out of 10”, “6” or may be read off a numerical scale. The risk score 30 may also be in the form of an image or any other visual representation. Risk mitigating factors and/or links to information relating to the risks associated with Johannesburg may be provided to assist the user in mitigating the risk associated with query 14. The user 16 may infer from the risk report 60 that there is a high risk of visiting or traveling to Johannesburg and that crime rates are high at certain times of the evening in specific areas of Johannesburg should be avoided.

Example 2: Where the subject of the query is a natural person

The user 16 inputs the query 14 “John Doe” into the input means 44 as a primary identifier and his identification number “12345678” as a secondary identifier. The searching means 18 searches for and retrieves information relating to “John Doe” and “12345678” from a variety of data sources 48. The retrieved information is validated by the validation means 56 and stored on the result database 50. The data is then filtered by the filtering means 24 to isolate the data relevant to pre-defined risk parameters 22 relating to a natural person, specifically to a person with the name John Doe. The risk parameters 22 may be law enforcement records such as criminal records, sex offender lists and the like; health information such as medical records, health insurance records, and the like; financial institution records such as credit history, credit scores, blacklisting, information relating to accounts and transactions, and the like; demographic information such as age, race, gender, education, employment status, the like. The filtered data is then weighted by the weighting means 26 in terms of impact and probability associated with the risk of the subject of the query 14. The calculating means 28 calculates a risk score 30 based on the weighted data and a risk report 60 is made available to the user 16 via the display device 58. The risk score 30 and other information may form part of the risk report 60 allows the user to assess the risk of John Doe. The risk score 30 for John Doe may be represented as a numerical value such as "20%”, “2 out of 10”, “2” or may be read off a numerical scale. The risk score 30 may also be in the form of an image or any other visual representation. Risk mitigating factors and/or links to information relating to the risks associated with John Doe may be provided to assist the user in mitigating the risk associated with query 14. The user 16 may infer from the risk report 60 that John Doe is a low-risk individual and that he does not have a criminal record and has been employed in the agricultural industry for several years.

Example 3: Where the subject of the query is a juristic person or organisation

The user 16 inputs the query 14 “Company ABC (Pty) Ltd.” into the input means 44. The searching means 18 searches for and retrieves information relating to “Company ABC (Pty) Ltd” from a variety of data sources 48. The retrieved information is validated by the validation means 56 and stored on the result database 50. The data is then filtered by the filtering means 24 to isolate the data relevant to pre-defined risk parameters 22 relating to a juristic person. These risk parameters 22 may be place of business, business structure such as corporation, sole proprietorship and the like, intellectual property ownership, previous business dealings, insurance, recruitment, links to other organisations, awards and accolades, reviews, blockchain, financial institution records such as financial status of the business, credit history, credit scores, blacklisting, information relating to accounts and transactions, and the like, community involvement, accreditation, legal status, employees, and the like. The filtered data is then weighted by the weighting means 26 in terms of impact and probability associated with the risk of the subject of the query 14. The calculating means 28 calculates a risk score 30 based on the weighted data and a risk report 60 is made available to the user 16 via the display device 58. The risk score 30 and other information may form part of the risk report 60 allows the user to assess the risk of Company ABC (Pty) Ltd. The risk score 30 for Company ABC (Pty) Ltd may be represented as a numerical value such as "10%”, “1 out of 10”, “1” or may be read off a numerical scale. The risk score 30 may also be in the form of an image or any other visual representation. Risk mitigating factors and/or links to information relating to the risks associated with Company ABC (Pty) Ltd may be provided to assist the user in mitigating the risk associated with query 14. The user 16 may infer from the risk report 60 that Company ABC (Pty) Ltd. is a very low-risk business that has no current claims against them, are not backlisted and have a strong position in the field of sales in South Africa.

Example 3: Where the subject of the query is a tangible object

The user 16 inputs the query 14 “Pineapple Smart Phone 13” into the input means 44. The searching means 18 searches for and retrieves information relating to “Pineapple Smart Phone 13” from a variety of data sources 48. The retrieved information is validated by the validation means 56 and stored on the result database 50. The data is then filtered by the filtering means 24 to isolate the data relevant to pre-defined risk parameters 22 relating to a tangible object. These risk parameters 22 may be price, health effects, side effects of use, usability, accessibility, availability, weight, mass, capacity, volume, size, impact on the environment, current value, resale value, storage instructions, use instructions, warnings associated with the object, security, lifespan of the object, disposal methods, and the like. The filtered data is then weighted by the weighting means 26 in terms of impact and probability associated with the risk of the subject of the query 14. The calculating means 28 calculates a risk score 30 based on the weighted data and a risk report 60 is made available to the user 16 via the display device 58. The risk score 30 and other information may form part of the risk report 60 allows the user to assess the risk of Pineapple Smart Phone 13. The risk score 30 for Pineapple Smart Phone 13 may be represented as a numerical value such as "50%”, “5 out of 10”, “5” or may be read off a numerical scale. The risk score 30 may also be in the form of an image or any other visual representation. Risk mitigating factors and/or links to information relating to the risks associated with Pineapple Smart Phone 13 may be provided to assist the user in mitigating the risk associated with query 14. The user 16 may infer from the risk report 60 that Pineapple Smart Phone 13 is medium-risk tangible object that is reasonably priced, has little to no health risks and has a low resale value.

Example 4: Where the subject of the query is an intangible object

The user 16 inputs the query 14 “trademark” into the input means 44. The searching means 18 searches for and retrieves information relating to “trademark” from a variety of data sources 48. The retrieved information is validated by the validation means 56 and stored on the result database 50. The data is then filtered by the filtering means 24 to isolate the data relevant to pre-defined risk parameters 22 relating to an intangible object. These risk parameters 22 may be price, current value, resale value, security, lifespan of the object, disposal, accessibility, availability, usability, effects of use, and the like. The filtered data is then weighted by the weighting means 26 in terms of impact and probability associated with the risk of the subject of the query 14. The calculating means 28 calculates a risk score 30 based on the weighted data and a risk report 60 is made available to the user 16 via the display device 58. The risk score 30 and other information may form part of the risk report 60 allows the user to assess the risk of a trademark. The risk score 30 for a trademark may be represented as a numerical value such as "0%”, “0 out of 10”, “0” or may be read off a numerical scale. The risk score 30 may also be in the form of an image or any other visual representation. Risk mitigating factors and/or links to information relating to the risks associated with a trademark may be provided to assist the user in mitigating the risk associated with query 14. The user 16 may infer from the risk report 60 that a trademark is a low-risk intangible object that is reasonably easy to obtain, has many benefits and has a long lifespan.

Example 5: Where the subject of the query is an activity

The user 16 inputs the query 14 “cave diving” into the input means 44. The searching means 18 searches for and retrieves information relating to “cave diving” from a variety of data sources 48. The retrieved information is validated by the validation means 56 and stored on the result database 50. The data is then filtered by the filtering means 24 to isolate the data relevant to pre-defined risk parameters 22 relating to an intangible object. These risk parameters 22 may be price, health effects, side effects of engaging in the activity, accessibility, availability, security, value, impact on the environment, safety warnings associated with the activity, and the like. The filtered data is then weighted by the weighting means 26 in terms of impact and probability associated with the risk of the subject of the query 14. The calculating means 28 calculates a risk score 30 based on the weighted data and a risk report 60 is made available to the user 16 via the display device 58. The risk score 30 and other information may form part of the risk report 60 allows the user to assess the risk of cave diving. The risk score 80 for a cave diving may be represented as a numerical value such as "80%”, “8 out of 10”, “8” or may be read off a numerical scale. The risk score 30 may also be in the form of an image or any other visual representation. Risk mitigating factors and/or links to information relating to the risks associated with cave diving may be provided to assist the user in mitigating the risk associated with query 14. The user 16 may infer from the risk report 60 that cave diving is a high-risk activity that is expensive, requires specialised equipment, is not accessible in certain areas and can lead to negative health effects.

It is, of course, to be appreciated that the risk management system in accordance with the invention is not limited to the precise constructional and functional details as hereinbefore described with reference to the accompanying drawings and which may be varied as desired.

Although only certain embodiments of the invention have been described herein, it will be understood by any person skilled in the art that other modifications, variations, and possibilities of the invention are possible. Such modifications, variations and possibilities are therefore to be considered as falling within the spirit and scope of the invention and hence form part of the invention as herein described and/or exemplified. It is further to be understood that the examples are provided for illustrating the invention further and to assist a person skilled in the art with understanding the invention and is not meant to be construed as unduly limiting the reasonable scope of the invention.

The inventor believes that the risk management system in accordance with the present invention is advantageous in that it provides a system which allows a risk score to be calculated for any subject and provides risk mitigating information related to the risk of the subject utilizing the computational power of Artificial Intelligence. This core feature reduces the manpower and technological costs associated with risk advisory services and early warning platforms currently available on the market. This makes the provision of critical risk and early public warning services possible to individual consumers, NGOs, small businesses and vulnerable communities and the like. This is a vital service in an increasingly turbulent world. The system can also be applied to the vetting of trade transactions involving parties in different territories who may not easily be able to carry out due diligence on one another before entering into a long-distance trade transaction. This will have the effect of enabling more small businesses to engage in transnational trade more securely.